This study aimed to evaluate the reliability and usefulness of information generated by Chat Generative Pre-Trained Transformer (ChatGPT) on temporomandibular joint disorders (TMD).

We asked ChatGPT about the diseases specified in the TMD classification and scored the responses using Likert reliability and usefulness scales, the modified DISCERN (mDISCERN) scale, and the Global Quality Scale (GQS).

The highest Likert scores for both reliability and usefulness were for masticatory muscle disorders (mean ± standard deviation [SD]: 6.0 ± 0), and the lowest scores were for inflammatory disorders of the temporomandibular joint (mean ± SD: 4.3 ± 0.6 for reliability, 4.0 ± 0 for usefulness). The median Likert reliability score indicates that the responses are highly reliable. The median Likert usefulness score was 5 (4-6), indicating that the responses were moderately useful. A comparative analysis was performed, and no statistically significant differences were found in any subject for either reliability or usefulness (P = 0.083-1.000). The median mDISCERN score was 4 (3-5) for the two raters. A statistically significant difference was observed in the mean mDISCERN scores between the two raters (P = 0.046). The GQS scores indicated a moderate to high quality (mean ± SD: 3.8 ± 0.8 for rater 1, 4.0 ± 0.5 for rater 2). No statistically significant correlation was found between mDISCERN and GQS scores (r = -0.006, P = 0.980).

Although ChatGPT-4 has significant potential, it can be used as an additional source of information regarding TMD for patients and clinicians.

Initial account interviews (IAi) offer eyewitnesses more immediate opportunities to answer a series of brief questions about their experiences prior to an in-depth, more formal investigative interview. An IAi is typically elicited in-person near/at the scene of a crime using broadly systematic questioning. Retrieval practice can improve subsequent recall in some contexts, but there is a dearth of research centred on the potential costs and benefits of a quick IAi. Furthermore, where an in-person IAi is impossible, no alternative quick provision exists. Given the systematic nature of the IAi protocol, we developed a conversational chatbot as a potential alternative. Using a mock-witness paradigm, we investigated the memory performance of adults from the general population during in-depth in-person interviews one week after having provided an IAi 10 min post event either (1) in person, (2) via the ChatCharlie chatbot, or (3) no IAi (control). IAi conditions leveraged significantly improved event recall during later investigative interviews versus the Control. Accounts were more accurate and complete, and more correct information was remembered without increased errors indicating the potential of digital agents for IAi purposes Findings concur with predictions from theoretical understanding of episodic memory consolidation and the empirical eyewitness literature regarding the benefits of practice in some contexts.

Artificial intelligence (AI) chatbots are novel computer programs that can generate text or content in a natural language format. Academic publishers are adapting to the transformative role of AI chatbots in producing or facilitating scientific research. This study aimed to examine the policies established by scientific, technical, and medical academic publishers for defining and regulating the authors' responsible use of AI chatbots.

This study performed a cross-sectional audit on the publicly available policies of 162 academic publishers, indexed as members of the International Association of the Scientific, Technical, and Medical Publishers (STM). Data extraction of publicly available policies on the webpages of all STM academic publishers was performed independently, in duplicate, with content analysis reviewed by a third contributor (September 2023-December 2023). Data was categorized into policy elements, such as 'proofreading' and 'image generation'. Counts and percentages of 'yes' (i.e., permitted), 'no', and 'no available information' (NAI) were established for each policy element.

A total of 56/162 (34.6%) STM academic publishers had a publicly available policy guiding the authors' use of AI chatbots. No policy allowed authorship for AI chatbots (or other AI tool). Most (49/56 or 87.5%) required specific disclosure of AI chatbot use. Four policies/publishers placed a complete ban on the use of AI chatbots by authors.

Only a third of STM academic publishers had publicly available policies as of December 2023. A re-examination of all STM members in 12-18 months may uncover evolving approaches toward AI chatbot use with more academic publishers having a policy.

Artificial intelligence is increasingly influencing healthcare, providing transformative opportunities and challenges for nursing practice. This review critically evaluates the integration of AI in nursing, focusing on its current applications, limitations, and areas that require further investigation. A comprehensive analysis of recent studies highlights the use of AI in clinical decision support systems, patient monitoring, and nursing education. However, several barriers to successful implementation are identified, including technical constraints, ethical dilemmas, and the need for workforce adaptation. Significant gaps in the literature are also evident, such as the limited development of nursing-specific AI tools, insufficient long-term impact assessments, and the absence of comprehensive ethical frameworks tailored to nursing contexts. The potential of AI to reshape personalized care, advance robotics in nursing, and address global health challenges is explored in depth. This review integrates existing knowledge and identifies critical areas for future research, emphasizing the necessity of aligning AI advancements with the specific needs of nursing. Addressing these gaps is essential to fully harness AI's potential while reducing associated risks, ultimately enhancing nursing practice and improving patient outcomes.

Immunotherapy has become a game changer in cancer treatment. The internet has been used by patients as a platform to share personal experiences and seek medical guidance. Despite the increased utilization of immunotherapy in clinical practice, few studies have investigated the perceptions about its use by analyzing social media data.

This study aims to use BERTopic (a topic modeling technique that is an extension of the Bidirectional Encoder Representation from Transformers machine learning model) to explore the perceptions of online cancer communities regarding immunotherapy.

A total of 4.9 million posts were extracted from Facebook, Twitter, Reddit, and 16 online cancer-related forums. The textual data were preprocessed by natural language processing. BERTopic modeling was performed to identify topics from the posts. The effectiveness of isolating topics from the posts was evaluated using 3 metrics: topic diversity, coherence, and quality. Sentiment analysis was performed to determine the polarity of each topic and categorize them as positive or negative. Based on the topics generated through topic modeling, thematic analysis was conducted to identify themes associated with immunotherapy.

After data cleaning, 3.6 million posts remained for modeling. The highest overall topic quality achieved by BERTopic was 70.47% (topic diversity: 87.86%; topic coherence: 80.21%). BERTopic generated 14 topics related to the perceptions of immunotherapy. The sentiment score of around 0.3 across the 14 topics suggested generally positive sentiments toward immunotherapy within the online communities. Six themes were identified, primarily covering (1) hopeful prospects offered by immunotherapy, (2) perceived effectiveness of immunotherapy, (3) complementary therapies or self-treatments, (4) financial and mental impact of undergoing immunotherapy, (5) impact on lifestyle and time schedules, and (6) side effects due to treatment.

This study provides an overview of the multifaceted considerations essential for the application of immunotherapy as a therapeutic intervention. The topics and themes identified can serve as supporting information to facilitate physician-patient communication and the decision-making process. Furthermore, this study also demonstrates the effectiveness of BERTopic in analyzing large amounts of data to identify perceptions underlying social media and online communities.

Digital tools could assist obstetric providers by delivering information given increasing options for fetal aneuploidy screening.

To determine the impact of a chatbot for pre-test education and counseling in low-risk pregnancies.

Two sites participated in this randomized controlled trial. Patients in the intervention group used a chatbot prior to the provider visit, while patients in the control group only received education by the provider. The primary outcome was change in patient knowledge scores after provider education. Analysis was by intention to treat.

Overall, 258 women participated (n = 130; intervention and n = 128; control). Knowledge gain was significantly higher among patients using the chatbot (mean increase in correct answers [out of 20]: +4.1 vs +1.9, p < 0.001). Both groups reported high satisfaction, with no statistically significant difference between intervention and control groups (mean patient satisfaction [1-10]: 8.2 vs 8.5 respectively, p = 0.35). Providers also reported high satisfaction scores with no significant difference between intervention and control groups (mean provider satisfaction [1 - 10]: 8.7 vs 8.4 respectively, p = 0.13).

Pre-test education via a chatbot can increase patient knowledge of prenatal testing choices, with high patient and provider satisfaction.

To explore the barriers and enablers to accessing diabetes eye care services among adults in Auckland.

This was a qualitative study that used semistructured interviews. We performed a thematic analysis and described the main barriers and enablers to accessing services using the Theoretical Domains Framework.

The study took place in two of the three public funding and planning agencies that provide primary and secondary health services in Auckland, the largest city in Aotearoa New Zealand.

Thirty people with diabetes in Auckland who had experienced interrupted diabetes eye care, having missed at least one appointment or being discharged back to their general practitioner after missing several appointments.

We identified barriers and enablers experienced by our predominantly Pacific and Māori participants that aligned with 7 (of the 14) domains in the Theoretical Domains Framework. The most reported barriers were transport issues, lack of awareness regarding the importance of retinal screening, time constraints, limited and/or inflexible appointment times and competing family commitments. Enablers included positive interactions with healthcare providers and timely appointment notifications and reminders.

Diabetes eye services could be made more responsive by addressing systemic barriers such as service location and transport links, appointment availability and meaningful information to aid understanding.

This systematic literature review aims to provide a comprehensive and structured synthesis of the existing knowledge about chatbots in healthcare from both a theoretical and methodological perspective.

To this end, a systematic literature review was conducted with 89 articles selected through a SPAR-4-SLR systematic procedure. The document for this systematic review was collected from Scopus database. The VoSviewer software facilitates the analysis of keyword co-occurrence to form the fundamental structure of the subject field.

In addition, this study proposes a future research agenda revolving around three main themes such as (1) telemedicine, (2) mental health and (3) medical information.

This study underscores the significance, implications and predictors of chatbot usage in healthcare services. It is concluded that adopting the proposed future direction and further research on chatbots in healthcare will help to refine chatbot systems to better meet the needs of patients.

Digital health interventions have emerged as promising tools to promote health behavior change and improve health outcomes. However, a comprehensive synthesis of strategies contributing to these interventions is lacking.

This study aims to (1) identify and categorize the strategies used in digital health interventions over the past 25 years; (2) explore the differences and changes in these strategies across time periods, countries, populations, delivery methods, and senders; and (3) serve as a valuable reference for future researchers and practitioners to improve the effectiveness of digital health interventions.

This study followed a systematic review approach, complemented by close reading and text coding. A comprehensive search for published English academic papers from PubMed, Web of Science, and Scopus was conducted. The search employed a combination of digital health and intervention-related terms, along with database-specific subject headings and filters. The time span covered 25 years, from January 1, 1999, to March 10, 2024. Sample papers were selected based on study design, intervention details, and strategies. The strategies were identified and categorized based on the principles of Behavior Change Techniques and Behavior Strategies.

A total of 885 papers involving 954,847 participants met the eligibility criteria. We identified 173 unique strategies used in digital health interventions, categorized into 19 themes. The 3 most frequently used strategies in the sample papers were "guide" (n=492, 55.6%), "monitor" (n=490, 55.4%), and "communication" (n=392, 44.3%). The number of strategies employed in each paper ranged from 1 to 32. Most interventions targeted clients (n=844, 95.4%) and were carried out in hospitals (n=268, 30.3%). High-income countries demonstrated a substantially higher number and diversity of identified strategies than low- and middle-income countries, and the number of studies targeting the public (n=647, 73.1%) far exceeded those focusing on vulnerable groups (n=238, 26.9%).

Digital health interventions and strategies have undergone considerable development over the past 25 years. They have evolved from simple approaches to sophisticated, personalized techniques and are trending toward multifaceted interventions, leveraging advanced technologies for real-time monitoring and feedback. Future studies should focus on rigorous evaluations, long-term effectiveness, and tailored approaches for diverse populations, and more attention should be given to vulnerable groups.

Chatbots are artificial intelligence (AI) programs designed to simulate conversations with humans that present opportunities and challenges in scientific research. Despite growing clarity from publishing organisations on the use of AI chatbots, researchers' perceptions remain less understood. In this international cross-sectional survey, we aimed to assess researchers' attitudes, familiarity, perceived benefits, and limitations related to AI chatbots. Our online survey was open from July 9 to Aug 11, 2023, with 61 560 corresponding authors identified from 122 323 articles indexed in PubMed. 2452 (4·0%) provided responses and 2165 (94·5%) of 2292 who met eligibility criteria completed the survey. 1161 (54·0%) of 2149 respondents were male and 959 (44·6%) were female. 1294 (60·5%) of 2138 respondents were familiar with AI chatbots, and 945 (44·5%) of 2125 had previously used AI chatbots in research. Only 244 (11·4%) of 2137 reported institutional training on AI tools, and 211 (9·9%) of 2131 noted institutional policies on AI chatbot use. Despite mixed opinions on the benefits, 1428 (69·7%) of 2048 expressed interest in further training. Although many valued AI chatbots for reducing administrative workload (1299 [66·9%] of 1941), there was insufficient understanding of the decision making process (1484 [77·2%] of 1923). Overall, this study highlights substantial interest in AI chatbots among researchers, but also points to the need for more formal training and clarity on their use.

This study aimed to evaluate the use of chatbot and video education to reduce anxiety in patients with breast cancer undergoing radiation therapy (RT).

This randomized controlled trial included patients with breast cancer scheduled for RT after surgery at an outpatient department of radiation oncology in a cancer center, randomly assigned to 4 groups: (1) video + chatbot, (2) video + paper, (3) paper + chatbot, and (4) paper + paper. In each group, patients received information regarding the treatment process and were frequently asked questions using the designated tool. Patient anxiety was evaluated using the Amsterdam preoperative anxiety and information scale (APAIS), state-trait anxiety inventory (STAI), and linear analog scale assessment (LASA) at 3 points: (1) initial outpatient visit (T0), (2) before the RT course (T1), and (3) after the RT course (T2). The primary endpoint was APAIS, and the secondaries were STAI and LASA. A mixed-model repeated-measures ANOVA was conducted with time as a within-group factor and treatment conditions as a between-group factor.

The final analysis included 145 patients. No significant interaction was observed between groups and time for the APAIS, STAI, or LASA. Although unplanned, analyses were conducted using the age of 50 years as the cutoff, based on a previous systematic review of digital literacy in the medical field. A trend toward reduced APAIS was found among patients aged ≤50 years who used the chatbot (per-protocol subgroup). In the video + chatbot group, the APAIS score decreased from 3.06 (T0) to 1.88 (T2); in contrast, in the paper + paper group, it decreased from 2.42 (T0) to 2.06 (T2). In contrast, no significant interaction was observed in the APAIS of per-protocol patients aged ≥50 years.

Overall, no significant differences were found in the effectiveness of different types of educational media in reducing patients' anxiety. However, for young patients who actively use video or chatbot resources, education through digital media may meaningfully reduce their anxiety during the RT process.

"Medical deserts" are areas with low healthcare service levels, challenging the access, quality, and sustainability of care. This qualitative narrative review examines how artificial intelligence (AI), particularly large language models (LLMs), can address these challenges by integrating with e-Health and the Internet of Medical Things to enhance services in under-resourced areas. It explores AI-driven telehealth platforms that overcome language and cultural barriers, increasing accessibility. The utility of LLMs in providing diagnostic assistance where specialist deficits exist is highlighted, demonstrating AI's role in supplementing medical expertise and improving outcomes. Additionally, the development of AI chatbots offers preliminary medical advice, serving as initial contact points in remote areas. The review also discusses AI's role in enhancing medical education and training, supporting the professional development of healthcare workers in these regions. It assesses AI's strategic use in data analysis for effective resource allocation, identifying healthcare provision gaps. AI, especially LLMs, is seen as a promising solution for bridging healthcare gaps in "medical deserts," improving service accessibility, quality, and distribution. However, continued research and development are essential to fully realize AI's potential in addressing the challenges of medical deserts.

Symptom checkers are designed for laypeople and promise to provide a preliminary diagnosis, a sense of urgency, and a suggested course of action.

We used the international symptom checker app (SCA) Ada App as an example to answer the following question: How do general practitioners (GPs) experience the SCA in relation to the macro, meso, and micro level of their daily work, and how does this interact with work-related psychosocial resources and demands?

We conducted 8 semistructured interviews with GPs in Germany between December 2020 and February 2022. We analyzed the data using the integrative basic method, an interpretative-reconstructive method, to identify core themes and modes of thematization.

Although most GPs in this study were open to digitization in health care and their practice, only one was familiar with the SCA. GPs considered the SCA as part of the "unorganized stage" of patients' searching about their conditions. Some preferred it to popular search engines. They considered it relevant to their work as soon as the SCA would influence patients' decisions to see a doctor. Some wanted to see the results of the SCA in advance in order to decide on the patient's next steps. GPs described the diagnostic process as guided by shared decision-making, with the GP taking the lead and the patient deciding. They saw diagnosis as an act of making sense of data, which the SCA would not be able to do, despite the huge amounts of data.

GPs took a techno-pragmatic view of SCA. They operate in a health care system of increasing scarcity. They saw the SCA as a potential work-related resource if it helped them to reduce administrative tasks and unnecessary patient contacts. The SCA was seen as a potential work-related demand if it increased workload, for example, if it increased patients' anxiety, was too risk-averse, or made patients more insistent on their own opinions.

In healthcare applications, AI-driven innovations are set to revolutionise patient interactions and care, with the aim of improving patient satisfaction. Recent advancements in Artificial Intelligence have significantly affected nursing, assistive management, medical diagnoses, and other critical medical procedures.

Many artificial intelligence (AI) solutions operate online, posing potential risks to patient data security. To address these security concerns and ensure swift operation, this study has developed a chatbot tailored for hospital environments, running on a local server, and utilising TinyML for processing patient data.

Edge computing technology enables secure on-site data processing. The implementation includes patient identification using a Histogram of Gradient (HOG)-based classification, followed by basic patient care tasks, such as temperature measurement and demographic recording.

The classification accuracy of patient detection was 95.8%. An autonomous temperature-sensing unit equipped with a medical-grade infrared temperature scanner detected and recorded patient temperature. Following the temperature assessment, the tinyML-powered chatbot engaged patients in a series of questions customised by doctors to train the model for diagnostic scenarios. Patients' responses, recorded as "yes" or "no", are stored and printed in their case sheet. The accuracy of the TinyML model is 95.3% and the on-device processing time is 217 ms. The implemented TinyML model uses only 8.8Kb RAM and 50.3Kb Flash memory, with a latency of only 4 ms.

Each patient was assigned a unique ID, and their data were securely stored for further consultation and diagnosis via hospital management. This research demonstrates faster patient data recording and increased security compared to existing AI-based healthcare solutions, as all processes occur within the local host.

The development of artificial intelligence (AI) and machine learning (ML)-based systems in medicine is growing, and these systems are being used for disease diagnosis, drug development, and treatment personalization. Some of these systems are designed to perform activities that demand human cognitive function. However, use of these systems in routine care by patients and caregivers lags behind expectations. This paper reviews several challenges that healthcare systems face and the obstacles of integrating digital systems into routine care. This paper focuses on integrating digital systems with human physicians. It describes second-generation AI systems designed to move closer to biology and reduce complexity, augmenting but not replacing physicians to improve patient outcomes. The constrained disorder principle (CDP) defines complex biological systems by their degree of regulated variability. This paper describes the CDP-based second-generation AI platform, which is the basis for the Digital Pill that is humanizing AI by moving closer to human biology via using the inherent variability of biological systems for improving outcomes. This system augments physicians, assisting them in decision-making to improve patients' responses and adherence but not replacing healthcare providers. It restores the efficacy of chronic drugs and improves adherence while generating data-driven therapeutic regimens. While AI can substitute for many medical activities, it is unlikely to replace human physicians. Human doctors will continue serving patients with capabilities augmented by AI. The described co-piloting model better reflects biological pathways and provides assistance to physicians for better care.

Generative artificial intelligence (AI) models are a new technological development with vast research use cases among medical subspecialties. These powerful large language models offer a wide range of possibilities in wound care, from personalized patient support to optimized treatment plans and improved scientific writing. They can also assist in efficiently navigating the literature and selecting and summarizing articles, enabling researchers to focus on impactful studies relevant to wound care management and enhancing response quality through prompt-learning iterations. For nonnative English-speaking medical practitioners and authors, generative AI may aid in grammar and vocabulary selection. Although reports have suggested limitations of the conversational agent on medical translation pertaining to the precise interpretation of medical context, when used with verified resources, this language model can breach language barriers and promote practice-changing advancements in global wound care. Further, AI-powered chatbots can enable continuous monitoring of wound healing progress and real-time insights into treatment responses through frequent, readily available remote patient follow-ups.However, implementing AI in wound care research requires careful consideration of potential limitations, especially in accurately translating complex medical terms and workflows. Ethical considerations are vital to ensure reliable and credible wound care research when using AI technologies. Although ChatGPT shows promise for transforming wound care management, the authors warn against overreliance on the technology. Considering the potential limitations and risks, proper validation and oversight are essential to unlock its true potential while ensuring patient safety and the effectiveness of wound care treatments.

Artificial intelligence (AI)-powered conversational agents are increasingly finding application in health care, as these can provide patient education at any time. However, their effectiveness in medical settings remains largely unexplored. This study aimed to assess the impact of the chatbot "PROState cancer Conversational Agent" (PROSCA), which was trained to provide validated support from diagnostic tests to treatment options for men facing prostate cancer (PC) diagnosis.

The chatbot PROSCA, developed by urologists at Heidelberg University Hospital and SAP SE, was evaluated through a randomized controlled trial (RCT). Patients were assigned to either the chatbot group, receiving additional access to PROSCA alongside standard information by urologists, or the control group (1:1), receiving standard information. A total of 112 men were included, of whom 103 gave feedback at study completion.

Over time, patients' information needs decreased significantly more in the chatbot group than in the control group (p = 0.035). In the chatbot group, 43/54 men (79.6%) used PROSCA, and all of them found it easy to use. Of the men, 71.4% agreed that the chatbot improved their informedness about PC and 90.7% would like to use PROSCA again. Limitations are study sample size, single-center design, and specific clinical application.

With the introduction of the PROSCA chatbot, we created and evaluated an innovative, evidence-based AI health information tool as an additional source of information for PC. Our RCT results showed significant benefits of the chatbot in reducing patients' information needs and enhancing their understanding of PC. This easy-to-use AI tool provides accurate, timely, and accessible support, demonstrating its value in the PC diagnosis process. Future steps include further customization of the chatbot's responses and integration with the existing health care systems to maximize its impact on patient outcomes.

This study evaluated an artificial intelligence-powered chatbot-PROSCA, a digital tool designed to support men facing prostate cancer diagnosis by providing validated information from diagnosis to treatment. Results showed that patients who used the chatbot as an additional tool felt better informed than those who received standard information from urologists. The majority of users appreciated the ease of use of the chatbot and expressed a desire to use it again; this suggests that PROSCA could be a valuable resource to improve patient understanding in prostate cancer diagnosis.

The need for increased clinical efficacy and efficiency has been the main force in developing artificial intelligence (AI) tools in medical imaging. The INCISIVE project is a European Union-funded initiative aiming to revolutionize cancer imaging methods using AI technology. It seeks to address limitations in imaging techniques by developing an AI-based toolbox that improves accuracy, specificity, sensitivity, interpretability, and cost-effectiveness.

To ensure the successful implementation of the INCISIVE AI service, a study was conducted to understand the needs, challenges, and expectations of health care professionals (HCPs) regarding the proposed toolbox and any potential implementation barriers.

A mixed methods study consisting of 2 phases was conducted. Phase 1 involved user experience (UX) design workshops with users of the INCISIVE AI toolbox. Phase 2 involved a Delphi study conducted through a series of sequential questionnaires. To recruit, a purposive sampling strategy based on the project's consortium network was used. In total, 16 HCPs from Serbia, Italy, Greece, Cyprus, Spain, and the United Kingdom participated in the UX design workshops and 12 completed the Delphi study. Descriptive statistics were performed using SPSS (IBM Corp), enabling the calculation of mean rank scores of the Delphi study's lists. The qualitative data collected via the UX design workshops was analyzed using NVivo (version 12; Lumivero) software.

The workshops facilitated brainstorming and identification of the INCISIVE AI toolbox's desired features and implementation barriers. Subsequently, the Delphi study was instrumental in ranking these features, showing a strong consensus among HCPs (W=0.741, P<.001). Additionally, this study also identified implementation barriers, revealing a strong consensus among HCPs (W=0.705, P<.001). Key findings indicated that the INCISIVE AI toolbox could assist in areas such as misdiagnosis, overdiagnosis, delays in diagnosis, detection of minor lesions, decision-making in disagreement, treatment allocation, disease prognosis, prediction, treatment response prediction, and care integration throughout the patient journey. Limited resources, lack of organizational and managerial support, and data entry variability were some of the identified barriers. HCPs also had an explicit interest in AI explainability, desiring feature relevance explanations or a combination of feature relevance and visual explanations within the toolbox.

The results provide a thorough examination of the INCISIVE AI toolbox's design elements as required by the end users and potential barriers to its implementation, thus guiding the design and implementation of the INCISIVE technology. The outcome offers information about the degree of AI explainability required of the INCISIVE AI toolbox across the three services: (1) initial diagnosis; (2) disease staging, differentiation, and characterization; and (3) treatment and follow-up indicated for the toolbox. By considering the perspective of end users, INCISIVE aims to develop a solution that effectively meets their needs and drives adoption.

To learn about the experiences of people who seek treatment for hard-to-heal wounds, we distributed a nationwide pilot survey, asking questions about the nature of their wound, how it shaped their daily lives, pathways to receiving care and experiences with treatment. The long-term objective is to quantify the journey of patients with hard-to-heal wounds to identify ideal intervention points that will lead to the best outcomes. This article summarises the findings, implications, limitations and suggestions for future research.

Qualitative data were self-reported from patients with hard-to-heal wounds (open for ≥4 weeks) in a pilot chatbot survey, (Wound Expert Survey (WES)) provided online in the US on Meta platforms (Facebook and Instagram) between 2021 and 2022.

The US national pilot survey attracted responses from 780 patients, 27 of whom provided a video testimonial. Some 57% of patients delayed treatment because they believed their wound would heal on its own, and only 4% saw a wound care specialist. Respondents reported the cost of care as the most frequent reason for not following all of a doctor's treatment recommendations. Queries regarding quality of life (QoL) revealed that more than half (65%) said they have negative thoughts associated with their wound at least every few days. Some 19% of respondents said their wound had an odour and, of them, 34% said odour had a major or severe negative impact on their self-confidence. Economically, nearly one-quarter of respondents said having a wound led to a drop in their total household income and 17% said their wound led to a change in their employment status.

A national pilot survey of patients with hard-to-heal wounds revealed that many delay seeking professional assistance and only a small minority see a wound care specialist. Experiencing an ulcer, even for a few months, can have significant negative effects on a patient's QoL. Patients frequently had negative thoughts associated with their wound, and odour compounded these negative effects, leading to major or severe negative impacts on self-confidence. Households experienced a decline in income, due to both the direct reduction or loss of patient employment and the additional time spent by family members assisting in patient recovery. Thus, a variety of factors contribute to poor outcomes for patients with hard-to-heal wounds. To validate and extend these preliminary results, future surveys of patients with hard-to-heal wounds should focus on additional reasons patients do not seek professional help sooner. To improve health outcomes and QoL, assessment of patient socioeconomic variables should occur whenever wound closure stalls.

We evaluated the performance of a chatbot compared to the National Comprehensive Cancer Network (NCCN) Guidelines for the management of ovarian cancer.

Using NCCN Guidelines, we generated 10 questions and answers regarding management of ovarian cancer at a single point in time. Questions were thematically divided into risk factors, surgical management, medical management, and surveillance. We asked ChatGPT (GPT-4) to provide responses without prompting (unprompted GPT) and with prompt engineering (prompted GPT). Responses were blinded and evaluated for accuracy and completeness by 5 gynecologic oncologists. A score of 0 was defined as inaccurate, 1 as accurate and incomplete, and 2 as accurate and complete. Evaluations were compared among NCCN, unprompted GPT, and prompted GPT answers.

Overall, 48% of responses from NCCN, 64% from unprompted GPT, and 66% from prompted GPT were accurate and complete. The percentage of accurate but incomplete responses was higher for NCCN vs GPT-4. The percentage of accurate and complete scores for questions regarding risk factors, surgical management, and surveillance was higher for GPT-4 vs NCCN; however, for questions regarding medical management, the percentage was lower for GPT-4 vs NCCN. Overall, 14% of responses from unprompted GPT, 12% from prompted GPT, and 10% from NCCN were inaccurate.

GPT-4 provided accurate and complete responses at a single point in time to a limited set of questions regarding ovarian cancer, with best performance in areas of risk factors, surgical management, and surveillance. Occasional inaccuracies, however, should limit unsupervised use of chatbots at this time.

In recent years, there has been an increase in the use of conversational agents for health promotion and service delivery. To date, health professionals' views on the use of this technology have received limited attention in the literature.

The purpose of this study was to gain a better understanding of how health professionals view the use of conversational agents for health care.

Physicians, nurses, and regulated mental health professionals were recruited using various web-based methods. Participants were interviewed individually using the Zoom (Zoom Video Communications, Inc) videoconferencing platform. Interview questions focused on the potential benefits and risks of using conversational agents for health care, as well as the best way to integrate conversational agents into the health care system. Interviews were transcribed verbatim and uploaded to NVivo (version 12; QSR International, Inc) for thematic analysis.

A total of 24 health professionals participated in the study (19 women, 5 men; mean age 42.75, SD 10.71 years). Participants said that the use of conversational agents for health care could have certain benefits, such as greater access to care for patients or clients and workload support for health professionals. They also discussed potential drawbacks, such as an added burden on health professionals (eg, program familiarization) and the limited capabilities of these programs. Participants said that conversational agents could be used for routine or basic tasks, such as screening and assessment, providing information and education, and supporting individuals between appointments. They also said that health professionals should have some oversight in terms of the development and implementation of these programs.

The results of this study provide insight into health professionals' views on the use of conversational agents for health care, particularly in terms of the benefits and drawbacks of these programs and how they should be integrated into the health care system. These collective findings offer useful information and guidance to stakeholders who have an interest in the development and implementation of this technology.

The emergence of digitalization and artificial intelligence has had a profound impact on society, especially in the field of medicine. Digital health is now a reality, with an increasing number of people using chatbots for prognostic or diagnostic purposes, therapeutic planning, and monitoring, as well as for nutritional and mental health support. Initially designed for various purposes, chatbots have demonstrated significant advantages in the medical field, as indicated by multiple sources. However, there are conflicting views in the current literature, with some sources highlighting their drawbacks and limitations, particularly in their use in oncology. This state-of-the-art review article seeks to present both the benefits and the drawbacks of chatbots in the context of medicine and cancer, while also addressing the challenges in their implementation, offering expert insights on the subject.

Cigarette smoking poses a major public health risk. Chatbots may serve as an accessible and useful tool to promote cessation due to their high accessibility and potential in facilitating long-term personalized interactions. To increase effectiveness and acceptability, there remains a need to identify and evaluate counseling strategies for these chatbots, an aspect that has not been comprehensively addressed in previous research.

This study aims to identify effective counseling strategies for such chatbots to support smoking cessation. In addition, we sought to gain insights into smokers' expectations of and experiences with the chatbot.

This mixed methods study incorporated a web-based experiment and semistructured interviews. Smokers (N=229) interacted with either a motivational interviewing (MI)-style (n=112, 48.9%) or a confrontational counseling-style (n=117, 51.1%) chatbot. Both cessation-related (ie, intention to quit and self-efficacy) and user experience-related outcomes (ie, engagement, therapeutic alliance, perceived empathy, and interaction satisfaction) were assessed. Semistructured interviews were conducted with 16 participants, 8 (50%) from each condition, and data were analyzed using thematic analysis.

Results from a multivariate ANOVA showed that participants had a significantly higher overall rating for the MI (vs confrontational counseling) chatbot. Follow-up discriminant analysis revealed that the better perception of the MI chatbot was mostly explained by the user experience-related outcomes, with cessation-related outcomes playing a lesser role. Exploratory analyses indicated that smokers in both conditions reported increased intention to quit and self-efficacy after the chatbot interaction. Interview findings illustrated several constructs (eg, affective attitude and engagement) explaining people's previous expectations and timely and retrospective experience with the chatbot.

The results confirmed that chatbots are a promising tool in motivating smoking cessation and the use of MI can improve user experience. We did not find extra support for MI to motivate cessation and have discussed possible reasons. Smokers expressed both relational and instrumental needs in the quitting process. Implications for future research and practice are discussed.

Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field.

This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations.

A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis.

The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts.

Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.

Patient-clinician communication and shared decision-making face challenges in the perioperative period. Chatbots have emerged as valuable support tools in perioperative care. A simultaneous and complete comparison of overall benefits and harm of chatbot application is conducted.

MEDLINE, EMBASE and the Cochrane Library were systematically searched for studies published before May 2023 on the benefits and harm of chatbots used in the perioperative period. The major outcomes assessed were patient satisfaction and knowledge acquisition. Untransformed proportion (PR) with a 95% CI was used for the analysis of continuous data. Risk of bias was assessed using the Cochrane Risk of Bias assessment tool version 2 and the Methodological Index for Non-Randomised Studies.

Eight trials comprising 1073 adults from four countries were included. Most interventions (n = 5, 62.5%) targeted perioperative care in orthopaedics. Most interventions use rule-based chatbots (n = 7, 87.5%). This meta-analysis found that the majority of the participants were satisfied with the use of chatbots (mean proportion=0.73; 95% CI: 0.62 to 0.85), and agreed that they gained knowledge in their perioperative period (mean proportion=0.80; 95% CI: 0.74 to 0.87).

This review demonstrates that perioperative chatbots are well received by the majority of patients with no reports of harm to-date. Chatbots may be considered as an aid in perioperative communication between patients and clinicians and shared decision-making. These findings may be used to guide the healthcare providers, policymakers and researchers for enhancing perioperative care.

The influential report Crossing the Quality Chasm: A New Health System for the 21st Century established six core objectives to enhance healthcare quality. It highlighted the necessity for healthcare to encompass safety, effectiveness, a patient-centered approach, timeliness, efficiency, and equity. This essay focuses on one of these six core objectives: a patient-centered approach. Healthcare leaders actively seek solutions to improve and ensure the delivery of high-quality care. The imperative to provide quality healthcare underscores the need for artificial intelligence (AI) to become an essential component in a patient-centered approach rather than merely an optional advantage. Despite the expansion of AI, there is a lack of understanding of how AI can improve patient-centered care. This essay examines the fundamental aspects of patient-centered care, as outlined by the Picker Institute, while also exploring the prospective role of AI in advancing the core principles of patient-centered care and proposing frameworks for applying AI in healthcare.

This study aimed to develop a retrained large language model (LLM) tailored to the needs of HN cancer patients treated with radiotherapy, with emphasis on symptom management and survivorship care.

A comprehensive external database was curated for training ChatGPT-4, integrating expert-identified consensus guidelines on supportive care for HN patients and correspondences from physicians and nurses within our institution's electronic medical records for 90 HN patients. The performance of our model was evaluated using 20 patient post-treatment inquiries that were then assessed by three Board certified radiation oncologists (RadOncs). The rating of the model was assessed on a scale of 1 (strongly disagree) to 5 (strongly agree) based on accuracy, clarity of response, completeness s, and relevance.

The average scores for the 20 tested questions were 4.25 for accuracy, 4.35 for clarity, 4.22 for completeness, and 4.32 for relevance, on a 5-point scale. Overall, 91.67% (220 out of 240) of assessments received scores of 3 or higher, and 83.33% (200 out of 240) received scores of 4 or higher.

The custom-trained model demonstrates high accuracy in providing support to HN patients offering evidence-based information and guidance on their symptom management and survivorship care.

Chatbots are increasingly being applied in the context of health care, providing access to services when there are constraints on human resources. Simple, rule-based chatbots are suited to high-volume, repetitive tasks and can therefore be used effectively in providing users with important health information. In this Viewpoint paper, we report on the implementation of a chatbot service called Ask Anxia as part of a wider provision of information and support services offered by the UK national charity, Anxiety UK. We reflect on the changes made to the chatbot over the course of approximately 18 months as the Anxiety UK team monitored its performance and responded to recurrent themes in user queries by developing further information and services. We demonstrate how corpus linguistics can contribute to the evaluation of user queries and the optimization of responses. On the basis of these observations of how Anxiety UK has developed its own chatbot service, we offer recommendations for organizations looking to add automated conversational interfaces to their services.

Artificial Intelligence has applications such as Large Language Models (LLMs), which simulate human-like conversations. The potential of LLMs in healthcare is not fully evaluated. This pilot study assessed the accuracy and consistency of chatbots and clinicians in answering common questions in pediatric dentistry.

Two expert pediatric dentists developed thirty true or false questions involving different aspects of pediatric dentistry. Publicly accessible chatbots (Google Bard, ChatGPT4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-instant-100k, and Google Palm) were employed to answer the questions (3 independent new conversations). Three groups of clinicians (general dentists, pediatric specialists, and students; n = 20/group) also answered. Responses were graded by two pediatric dentistry faculty members, along with a third independent pediatric dentist. Resulting accuracies (percentage of correct responses) were compared using analysis of variance (ANOVA), and post-hoc pairwise group comparisons were corrected using Tukey's HSD method. ACronbach's alpha was calculated to determine consistency.

Pediatric dentists were significantly more accurate (mean±SD 96.67 %± 4.3 %) than other clinicians and chatbots (p < 0.001). General dentists (88.0 % ± 6.1 %) also demonstrated significantly higher accuracy than chatbots (p < 0.001), followed by students (80.8 %±6.9 %). ChatGPT showed the highest accuracy (78 %±3 %) among chatbots. All chatbots except ChatGPT3.5 showed acceptable consistency (Cronbach alpha>0.7).

Based on this pilot study, chatbots may be valuable adjuncts for educational purposes and for distributing information to patients. However, they are not yet ready to serve as substitutes for human clinicians in diagnostic decision-making.

In this pilot study, chatbots showed lower accuracy than dentists. Chatbots may not yet be recommended for clinical pediatric dentistry.

Despite the numerous potential benefits of health chatbots for personal health management, a substantial proportion of people oppose the use of such software applications. Building on the innovation resistance theory (IRT) and the prototype willingness model (PWM), this study investigated the functional barriers, psychological barriers, and negative prototype perception antecedents of individuals' resistance to health chatbots, as well as the rational and irrational psychological mechanisms underlying their linkages.

Data from 398 participants were used to construct a partial least squares structural equation model (PLS-SEM).

Resistance intention mediated the relationship between functional barriers, psychological barriers, and resistance behavioral tendency, respectively. Furthermore, The relationship between negative prototype perceptions and resistance behavioral tendency was mediated by resistance intention and resistance willingness. Moreover, negative prototype perceptions were a more effective predictor of resistance behavioral tendency through resistance willingness than functional and psychological barriers.

By investigating the role of irrational factors in health chatbot resistance, this study expands the scope of the IRT to explain the psychological mechanisms underlying individuals' resistance to health chatbots. Interventions to address people's resistance to health chatbots are discussed.

Large language models (LLMs) have shown promise for task-oriented dialogue across a range of domains. The use of LLMs in health and fitness coaching is under-explored. Behavior science frameworks such as COM-B, which conceptualizes behavior change in terms of capability (C), Opportunity (O) and Motivation (M), can be used to architect coaching interventions in a way that promotes sustained change. Here we aim to incorporate behavior science principles into an LLM using two knowledge infusion techniques: coach message priming (where exemplar coach responses are provided as context to the LLM), and dialogue re-ranking (where the COM-B category of the LLM output is matched to the inferred user need). Simulated conversations were conducted between the primed or unprimed LLM and a member of the research team, and then evaluated by 8 human raters. Ratings for the primed conversations were significantly higher in terms of empathy and actionability. The same raters also compared a single response generated by the unprimed, primed and re-ranked models, finding a significant uplift in actionability and empathy from the re-ranking technique. This is a proof of concept of how behavior science frameworks can be infused into automated conversational agents for a more principled coaching experience.

Optimal management of allergic rhinitis requires patient education with easy access to accurate information. However, previous online platforms have provided misleading information. The demand for online medical information continues to grow, especially with the introduction of advanced chatbots like ChatGPT.

This study aimed to evaluate the quality of information provided by ChatGPT regarding allergic rhinitis. A Likert scale was used to assess the accuracy of responses, ranging from 1 to 5. Four authors independently rated the responses from a healthcare professional's perspective.

A total of 20 questions covering various aspects of allergic rhinitis were asked. Among the answers, eight received a score of 5 (no inaccuracies), five received a score of 4 (minor non-harmful inaccuracies), six received a score of 3 (potentially misinterpretable inaccuracies) and one answer had a score of 2 (minor potentially harmful inaccuracies).

The variability in accuracy scores highlights the need for caution when relying solely on chatbots like ChatGPT for medical advice. Patients should consult qualified healthcare professionals and use online sources as a supplement. While ChatGPT has advantages in medical information delivery, its use should be approached with caution. ChatGPT can be useful for patient education but cannot replace healthcare professionals.

Conversational agents (CAs) with emerging artificial intelligence present new opportunities to assist in health interventions but are difficult to evaluate, deterring their applications in the real world. We aimed to synthesize existing evidence and knowledge and outline an evaluation framework for CA interventions.

We conducted a systematic scoping review to investigate designs and outcome measures used in the studies that evaluated CAs for health interventions. We then nested the results into an overarching digital health framework proposed by the World Health Organization (WHO).

The review included 81 studies evaluating CAs in experimental (n = 59), observational (n = 15) trials, and other research designs (n = 7). Most studies (n = 72, 89%) were published in the past 5 years. The proposed CA-evaluation framework includes 4 evaluation stages: (1) feasibility/usability, (2) efficacy, (3) effectiveness, and (4) implementation, aligning with WHO's stepwise evaluation strategy. Across these stages, this article presents the essential evidence of different study designs (n = 8), sample sizes, and main evaluation categories (n = 7) with subcategories (n = 40). The main evaluation categories included (1) functionality, (2) safety and information quality, (3) user experience, (4) clinical and health outcomes, (5) costs and cost benefits, (6) usage, adherence, and uptake, and (7) user characteristics for implementation research. Furthermore, the framework highlighted the essential evaluation areas (potential primary outcomes) and gaps across the evaluation stages.

This review presents a new framework with practical design details to support the evaluation of CA interventions in healthcare research.

The Open Science Framework (https://osf.io/9hq2v) on March 22, 2021.

Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings.

This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health.

In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks.

The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024.

Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users.

PRR1-10.2196/54349.

The accumulation of vast electronic medical records (EMRs) through medical informatization creates significant research value, particularly in obstetrics. Diagnostic standardization across different health care institutions and regions is vital for medical data analysis. Large language models (LLMs) have been extensively used for various medical tasks. Prompt engineering is key to use LLMs effectively.

This study aims to evaluate and compare the performance of LLMs with various prompt engineering techniques on the task of standardizing obstetric diagnostic terminology using real-world obstetric data.

The paper describes a 4-step approach used for mapping diagnoses in electronic medical records to the International Classification of Diseases, 10th revision, observation domain. First, similarity measures were used for mapping the diagnoses. Second, candidate mapping terms were collected based on similarity scores above a threshold, to be used as the training data set. For generating optimal mapping terms, we used two LLMs (ChatGLM2 and Qwen-14B-Chat [QWEN]) for zero-shot learning in step 3. Finally, a performance comparison was conducted by using 3 pretrained bidirectional encoder representations from transformers (BERTs), including BERT, whole word masking BERT, and momentum contrastive learning with BERT (MC-BERT), for unsupervised optimal mapping term generation in the fourth step.

LLMs and BERT demonstrated comparable performance at their respective optimal levels. LLMs showed clear advantages in terms of performance and efficiency in unsupervised settings. Interestingly, the performance of the LLMs varied significantly across different prompt engineering setups. For instance, when applying the self-consistency approach in QWEN, the F1-score improved by 5%, with precision increasing by 7.9%, outperforming the zero-shot method. Likewise, ChatGLM2 delivered similar rates of accurately generated responses. During the analysis, the BERT series served as a comparative model with comparable results. Among the 3 models, MC-BERT demonstrated the highest level of performance. However, the differences among the versions of BERT in this study were relatively insignificant.

After applying LLMs to standardize diagnoses and designing 4 different prompts, we compared the results to those generated by the BERT model. Our findings indicate that QWEN prompts largely outperformed the other prompts, with precision comparable to that of the BERT model. These results demonstrate the potential of unsupervised approaches in improving the efficiency of aligning diagnostic terms in daily research and uncovering hidden information values in patient data.

Examining information presentation strategies that may facilitate patient education through patient portals is important for effective health education.

A randomized exploratory study evaluated information presentation (text or videos) and a chatbot in patient education and examined several performance and outcome variables (e.g., search duration, Decisional Conflict Scale, and eye-tracking measures), along with a simple descriptive qualitative content analysis of the transcript of chatbot.

Of the 92 participants, those within the text conditions (n = 46, p < 0.001), had chatbot experiences (B =-74.85, p = 0.046), knew someone with IBD (B =-98.66, p = 0.039), and preferred to engage in medical decision-making (B =102.32, p = 0.006) were more efficient in information-searching. Participants with videos spent longer in information-searching (mean=666.5 (SD=171.6) VS 480.3 (SD=159.5) seconds, p < 0.001) but felt more informed (mean score=18.8 (SD=17.6) VS 27.4 (SD=18.9), p = 0.027). The participants' average eye fixation duration with videos was significantly higher (mean= 473.8 ms, SD=52.9, p < 0.001).

Participants in video conditions were less efficient but more effective in information seeking. Exploring the trade-offs between efficiency and effectiveness for user interface designs is important to appropriately deliver education within patient portals.

This study suggests that user interface designs and chatbots impact health information's efficiency and effectiveness.

Introduction With the expanding awareness and use of AI-powered chatbots, it seems possible that an increasing number of people could use them to assess and evaluate their medical symptoms. If chatbots are used for this purpose, that have not previously undergone a thorough medical evaluation for this specific use, various risks might arise. The aim of this study is to analyze and compare the performance of popular chatbots in differentiating between severe and less critical medical symptoms described from a patient's perspective and to examine the variations in substantive medical assessment accuracy and empathetic communication style among the chatbots' responses. Materials and methods Our study compared three different AI-supported chatbots - OpenAI's ChatGPT 3.5, Microsoft's Bing Chat, and Inflection's Pi AI. Three exemplary case reports for medical emergencies as well as three cases without an urgent reason for an emergency medical admission were constructed and analyzed. Each case report was accompanied by identical questions concerning the most likely suspected diagnosis and the urgency of an immediate medical evaluation. The respective answers of the chatbots were qualitatively compared with each other regarding the medical accuracy of the differential diagnoses mentioned and the conclusions drawn, as well as regarding patient-oriented and empathetic language. Results All examined chatbots were capable of providing medically plausible and probable diagnoses and classifying situations as acute or less critical. However, their responses varied slightly in the level of their urgency assessment. Clear differences could be seen in the level of detail of the differential diagnoses, the overall length of the answers, and how the chatbot dealt with the challenge of being confronted with medical issues. All given answers were comparable in terms of empathy level and comprehensibility. Conclusion Even AI chatbots that are not designed for medical applications already offer substantial guidance in assessing typical medical emergency indications but should always be provided with a disclaimer. In responding to medical queries, characteristic differences emerge among chatbots in the extent and style of their respective answers. Given the lack of medical supervision of many established chatbots, subsequent studies, and experiences are essential to clarify whether a more extensive use of these chatbots for medical concerns will have a positive impact on healthcare or rather pose major medical risks.

Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.

Artificial intelligence (AI) chatbots have emerged as a potential tool to transform urology by improving patient care and physician efficiency. With an emphasis on their potential advantages and drawbacks, this literature review offers a thorough assessment of the state of AI-driven chatbots in urology today.

The capacity of AI-driven chatbots in urology to give patients individualized and timely medical advice is one of its key advantages. Chatbots can help patients prioritize their symptoms and give advice on the best course of treatment. By automating administrative duties and offering clinical decision support, chatbots can also help healthcare providers. Before chatbots are widely used in urology, there are a few issues that need to be resolved. The precision of chatbot diagnoses and recommendations might be impacted by technical constraints like system errors and flaws. Additionally, issues regarding the security and privacy of patient data must be resolved, and chatbots must adhere to all applicable laws. Important issues that must be addressed include accuracy and dependability because any mistakes or inaccuracies could seriously harm patients. The final obstacle is resistance from patients and healthcare professionals who are hesitant to use new technology or who value in-person encounters. AI-driven chatbots have the potential to significantly improve urology care and efficiency. However, it is essential to thoroughly test and ensure the accuracy of chatbots, address privacy and security concerns, and design user-friendly chatbots that can integrate into existing workflows. By exploring various scenarios and examining the current literature, this review provides an analysis of the prospects and limitations of implementing chatbots in urology.

Millions of people in the UK have asthma, yet 70% do not access basic care, leading to the largest number of asthma-related deaths in Europe. Chatbots may extend the reach of asthma support and provide a bridge to traditional healthcare. This study evaluates 'Brisa', a chatbot designed to improve asthma patients' self-assessment and self-management.

We recruited 150 adults with an asthma diagnosis to test our chatbot. Participants were recruited over three waves through social media and a research recruitment platform. Eligible participants had access to 'Brisa' via a WhatsApp or website version for 28 days and completed entry and exit questionnaires to evaluate user experience and asthma control. Weekly symptom tracking, user interaction metrics, satisfaction measures, and qualitative feedback were utilised to evaluate the chatbot's usability and potential effectiveness, focusing on changes in asthma control and self-reported behavioural improvements.

74% of participants engaged with 'Brisa' at least once. High task completion rates were observed: asthma attack risk assessment (86%), voice recording submission (83%) and asthma control tracking (95.5%). Post use, an 8% improvement in asthma control was reported. User satisfaction surveys indicated positive feedback on helpfulness (80%), privacy (87%), trustworthiness (80%) and functionality (84%) but highlighted a need for improved conversational depth and personalisation.

The study indicates that chatbots are effective for asthma support, demonstrated by the high usage of features like risk assessment and control tracking, as well as a statistically significant improvement in asthma control. However, lower satisfaction in conversational flexibility highlights rising expectations for chatbot fluency, influenced by advanced models like ChatGPT. Future health-focused chatbots must balance conversational capability with accuracy and safety to maintain engagement and effectiveness.

This study aimed to assessing usability of intelligent guidance chatbots (IGCs) in Chinese hospitals.

A cross-sectional study based on expert survey was conducted between August to December 2023. The survey assessed the usability of chatbots in 590 Chinese hospitals. One-way ANOVA was used to analyze the impact of the number of functions, human-like characteristics, number of outpatients, and staff size on the usability of the IGCs.

The results indicate that there are 273 (46.27%) hospitals scoring above 45 points. In terms of function development, 581(98.47%) hospitals have set the number of functions between 1 and 5. Besides, 350 hospitals have excellent function implementation, accounting for 59.32%. In terms of the IGC's human-like characteristic, 220 hospitals have both an avatar and a nickname. Results of One-way ANOVA show that, the number of functions(F = 202.667, P < 0.001), human-like characteristics(F = 372.29, P < 0.001), staff size(F = 9.846, P < 0.001), and the number of outpatients(F = 5.709, P = 0.004) have significant impact on the usability of hospital IGCs.

This study found that the differences in the usability of hospital IGCs at various levels of the number of functions, human-like characteristics, number of outpatients, and staff size. These findings provide insights for deploying hospital IGCs and can inform improvements in patient's experience and adoption of chatbots.

The COVID-19 pandemic influenced many to consider methods to reduce human contact and ease the burden placed on health care workers. Conversational agents or chatbots are a set of technologies that may aid with these challenges. They may provide useful interactions for users, potentially reducing the health care worker burden while increasing user satisfaction. Research aims to understand these potential impacts of chatbots and conversational recommender systems and their associated design features.

The objective of this study was to evaluate user perceptions of the helpfulness of an artificial intelligence chatbot that was offered free to the public in response to COVID-19. The chatbot engaged patients and provided educational information and the opportunity to report symptoms, understand personal risks, and receive referrals for care.

A cross-sectional study design was used to analyze 82,222 chats collected from patients in South Carolina seeking services from the Prisma Health system. Chi-square tests and multinomial logistic regression analyses were conducted to assess the relationship between reported risk factors and perceived chat helpfulness using chats started between April 24, 2020, and April 21, 2022.

A total of 82,222 chat series were started with at least one question or response on record; 53,805 symptom checker questions with at least one COVID-19-related activity series were completed, with 5191 individuals clicking further to receive a virtual video visit and 2215 clicking further to make an appointment with a local physician. Patients who were aged >65 years (P<.001), reported comorbidities (P<.001), had been in contact with a person with COVID-19 in the last 14 days (P<.001), and responded to symptom checker questions that placed them at a higher risk of COVID-19 (P<.001) were 1.8 times more likely to report the chat as helpful than those who reported lower risk factors. Users who engaged with the chatbot to conduct a series of activities were more likely to find the chat helpful (P<.001), including seeking COVID-19 information (3.97-4.07 times), in-person appointments (2.46-1.99 times), telehealth appointments with a nearby provider (2.48-1.9 times), or vaccination (2.9-3.85 times) compared with those who did not perform any of these activities.

Chatbots that are designed to target high-risk user groups and provide relevant actionable items may be perceived as a helpful approach to early contact with the health system for assessing communicable disease symptoms and follow-up care options at home before virtual or in-person contact with health care providers. The results identified and validated significant design factors for conversational recommender systems, including triangulating a high-risk target user population and providing relevant actionable items for users to choose from as part of user engagement.

Recent Artificial Intelligence (AI) advancements have facilitated tools capable of generating digital twins of real human faces and voices for interactive communication. In this research, we explore utilizing Digital Twins of Doctors (DTDs) in healthcare because using a doctor's identity can provide benefits like enhancing the credibility of the health information delivered using computers. DTDs are computer-controlled AI-generated digital replicas of doctors that closely resemble their characteristics. However, there exist limitations, including the social implications of using a doctor's identity, potential negative impacts on doctor-patient communication, and liability concerns. To ensure a comprehensive understanding of DTD usage in healthcare before widespread adoption, systematic research is essential. As a step towards this direction, in this qualitative research, we report findings from 13 semi-structured interviews with doctors. Our findings indicate that doctors believe DTDs offer benefits by saving doctors' time through the efficient delivery of repetitive information and personalizing patient care. Moreover, while using a doctor's identity can enhance credibility, it also raises concerns about using a doctor's identity to spread potential misinformation. These findings contribute by informing future researchers about doctors' perspectives on utilizing DTDs in healthcare, guiding the development of effective implementation strategies for responsible DTD integration into healthcare.

To analyze the clinical completeness, correctness, usefulness, and safety of chatbot and medication database responses to everyday inpatient medication-use questions.

We evaluated the responses from an artificial intelligence chatbot, a medication database, and clinical pharmacists to 200 real-world medication-use questions. Answer quality was rated by a blinded group of pharmacists, providers, and nurses. Chatbot and medication database responses were deemed "acceptable" if the mean reviewer rating was within 3 points of the mean rating for pharmacists' answers. We used descriptive statistics for reviewer ratings and Kendall's coefficient to evaluate interrater agreement.

The medication database generated responses to 194 (97%) questions, with 88% considered acceptable for clinical correctness, 76% considered acceptable for completeness, 83% considered acceptable for safety, and 81% considered acceptable for usefulness compared to pharmacists' answers. The chatbot responded to only 160 (80%) questions, with 85% considered acceptable for clinical correctness, 65% considered acceptable for completeness, 71% considered acceptable for safety, and 68% considered acceptable for usefulness.

Traditional search methods using a drug database provide more clinically correct, complete, safe, and useful answers than a chatbot. When the chatbot generated a response, the clinical correctness was similar to that of a drug database; however, it was not rated as favorably for clinical completeness, safety, or usefulness. Our results highlight the need for ongoing training and continued improvements to artificial intelligence chatbots for them to be incorporated reliably into the clinical workflow. With continued improvement in chatbot functionality, chatbots could be a useful pharmacist adjunct, providing healthcare providers with quick and reliable answers to medication-use questions.