Access to artificial intelligence is ubiquitous but its limitations in the preparation of scholarly articles have not been established in implant restorative treatment planning.

The purpose of this study is to determine if ChatGPT can be a reliable resource in synthesizing the best available literature on treatment planning questions for implant-supported prostheses.

Six questions were posed to ChatGPT on treatment planning implant-supported prostheses for the partially edentulous and completely edentulous scenario. Question 1: Would higher crown to implant ratios (C/I) greater than 1:1 be linked to increased marginal bone loss? Question 2: Do 2-unit posterior cantilevers lead to more bone loss than 2 adjacent implants? Question 3: Should implants be splinted in the posterior maxilla in patients that require no grafting and are not bruxers? Question 4: Do patients prefer a maxillary implant overdenture to a well-made complete denture? Question 5: Do resilient and rigid anchorage systems have the same maintenance when comparing implant overdentures? Question 6: Do denture patients prefer fixed implant prostheses compared with removable implant prostheses? Follow-up questions were intended to clarify the source and content of the supporting evidence for ChatGPT's responses. Additional higher quality and timely studies indexed on PubMed were identified for ChatGPT to consider in a revision of its original implant treatment planning answer. A quantitative rating was assessed based on 4 indices: accurate/retrievable source, representative literature, accurate interpretation of evidence, original conclusion reflects best evidence.

ChatGPT's responses to: Question 1: "Higher C/I can be associated with an increased risk of marginal bone loss." Revision: "While many clinicians believe that higher C/I ratios lead to bone loss, recent evidence suggests that this concern is less relevant for modern implants." Question 2: "The presence of cantilever extensions with short implants tend to fail at earlier time points and has been associated with a higher incidence of technical complications. Revision: "The use of implant-supported single-unit crowns with cantilever extensions in posterior regions is a viable long-term treatment option with minimal complications." Question 3: "Splinted restorations were associated with a higher implant survival rate, particularly in the posterior region." Revision: "There is no compelling evidence to suggest that splinting all implants in the posterior maxilla is necessary." Question 4: Patients report higher satisfaction with maxillary implant-supported overdentures compared to conventional complete dentures. Revision: "For patients with adequate maxillary bone support, a conventional denture may be just as satisfactory as an implant overdenture." Question 5: "While resilient attachments may require more frequent replacement of components, rigid attachments might necessitate monitoring for implant-related complications due to increased stress." Revision: "Research indicates that rigid attachment systems, such as bar and telescopic attachments, do not necessarily lead to increased complications due to stress in implant overdentures." Question 6: "Yes, in general, denture patients tend to prefer fixed implant prostheses over removable implant prostheses due to several key advantages. However, preferences can vary based on individual needs, costs, and clinical factors." Revision: "There is no universal patient preference for fixed or removable implant prostheses. Satisfaction is generally high with both options, and preference depends on individual patient factors, including comfort, hygiene, cost, and anatomical considerations."

ChatGPT has not demonstrated the ability to accurately cull the literature, stratify the rigor of the evidence, and extract accurate implications from the studies selected to deliver the best evidence-based answers to questions on treatment planning implant-supported prostheses.

To evaluate the extent to which experienced reviewers can accurately discern between artificial intelligence (AI)-generated and original research abstracts published in the field of shoulder and elbow surgery and compare this with the performance of an AI detection tool.

Twenty-five shoulder- and elbow-related articles published in high-impact journals in 2023 were randomly selected. ChatGPT was prompted with only the abstract title to create an AI-generated version of each abstract. The resulting 50 abstracts were randomly distributed to and evaluated by 8 blinded peer reviewers with at least 5 years of experience. Reviewers were tasked with distinguishing between original and AI-generated text. A Likert scale assessed reviewer confidence for each interpretation, and the primary reason guiding assessment of generated text was collected. AI output detector (0%-100%) and plagiarism (0%-100%) scores were evaluated using GPTZero.

Reviewers correctly identified 62% of AI-generated abstracts and misclassified 38% of original abstracts as being AI generated. GPTZero reported a significantly higher probability of AI output among generated abstracts (median, 56%; interquartile range [IQR], 51%-77%) compared with original abstracts (median, 10%; IQR, 4%-37%; P < .01). Generated abstracts scored significantly lower on the plagiarism detector (median, 7%; IQR, 5%-14%) relative to original abstracts (median, 82%; IQR, 72%-92%; P < .01). Correct identification of AI-generated abstracts was predominately attributed to the presence of unrealistic data/values. The primary reason for misidentifying original abstracts as AI was attributed to writing style.

Experienced reviewers faced difficulties in distinguishing between human and AI-generated research content within shoulder and elbow surgery. The presence of unrealistic data facilitated correct identification of AI abstracts, whereas misidentification of original abstracts was often ascribed to writing style.

With rapidly increasing AI advancements, it is paramount that ethical standards of scientific reporting are upheld. It is therefore helpful to understand the ability of reviewers to identify AI-generated content.

This investigation systematically reviews the recognition of generative AI tools, particularly ChatGPT, in scholarly literature. Utilizing 1,226 publications from the Dimensions database, ranging from November 2022 to July 2023, the research scrutinizes temporal trends and distribution across disciplines and regions. U.S.-based authors lead in acknowledgments, with notable contributions from China and India. Predominantly, Biomedical and Clinical Sciences, as well as Information and Computing Sciences, are engaging with these AI tools. Publications like "The Lancet Digital Health" and platforms such as "bioRxiv" are recurrent venues for such acknowledgments, highlighting AI's growing impact on research dissemination. The analysis is confined to the Dimensions database, thus potentially overlooking other sources and grey literature. Additionally, the study abstains from examining the acknowledgments' quality or ethical considerations. Findings are beneficial for stakeholders, providing a basis for policy and scholarly discourse on ethical AI use in academia. This study represents the inaugural comprehensive empirical assessment of AI acknowledgment patterns in academic contexts, addressing a previously unexplored aspect of scholarly communication.

To examine the abilities of ChatGPT in writing scientific ophthalmology introductions and to compare those abilities to experienced ophthalmologists.

OpenAI web interface was utilized to interact with and prompt ChatGPT 4 for generating the introductions for the selected papers. Consequently, each paper had two introductions-one drafted by ChatGPT and the other by the original author. Ten ophthalmology specialists with a minimal experience of more than 15 years, each representing distinct subspecialties-retina, neuro-ophthalmology, oculoplastic, glaucoma, and ocular oncology were provided with the two sets of introductions without revealing the origin (ChatGPT or human author) and were tasked to evaluate the introductions.

For each type of introduction, out of 45 instances, specialists correctly identified the source 26 times (57.7%) and erred 19 times (42.2%). The misclassification rates for introductions were 25% for experts evaluating introductions from their own subspecialty while to 44.4% for experts assessed introductions outside their subspecialty domain. In the comparative evaluation of introductions written by ChatGPT and human authors, no significant difference was identified across the assessed metrics (language, data arrangement, factual accuracy, originality, data Currency). The misclassification rate (the frequency at which reviewers incorrectly identified the authorship) was highest in Oculoplastic (66.7%) and lowest in Retina (11.1%).

ChatGPT represents a significant advancement in facilitating the creation of original scientific papers in ophthalmology. The introductions generated by ChatGPT showed no statistically significant difference compared to those written by experts in terms of language, data organization, factual accuracy, originality, and the currency of information. In addition, nearly half of them being indistinguishable from the originals. Future research endeavours should explore ChatGPT-4's utility in composing other sections of research papers and delve into the associated ethical considerations.

This study evaluated the ability to differentiate between AI-generated and human-authored abstracts in foot and ankle surgery.

An AI system (ChatGPT 3.0) was trained on 21 published abstracts to create six novel case abstracts. Nine foot and ankle surgeons participated in a blinded survey, tasked with distinguishing AI-generated from human-written abstracts, rating their confidence in their responses. Surveys were completed twice at two different time points to evaluate intra-/inter-observer reliability.

The overall accuracy rate for distinguishing AI-generated from human-written abstracts was 50.5 % (n = 109/216), indicating no better performance than random chance. Reviewer experience and AI familiarity did not significantly affect accuracy. Inter-rater reliability was moderate initially but decreased over time, and intra-rater reliability was poor.

In their current form, AI-generated abstracts are nearly indistinguishable from human-written ones, posing challenges for consistent identification in foot and ankle surgery.

IV.

Artificial intelligence (AI) has become integral to medical research, impacting various aspects such as data analysis, writing assistance, and publishing. This paper explores the multifaceted influence of AI on the process of writing medical research papers, encompassing data analysis, ethical considerations, writing assistance, and publishing efficiency.

The review was conducted following the PRISMA guidelines; a comprehensive search was performed in Scopus, PubMed, EMBASE, and MEDLINE databases for research publications on artificial intelligence in medical research published up to October 2023.

AI facilitates the writing process by generating drafts, offering grammar and style suggestions, and enhancing manuscript quality through advanced models like ChatGPT. Ethical concerns regarding content ownership and potential biases in AI-generated content underscore the need for collaborative efforts among researchers, publishers, and AI creators to establish ethical standards. Moreover, AI significantly influences data analysis in healthcare, optimizing outcomes and patient care, particularly in fields such as obstetrics and gynecology and pharmaceutical research. The application of AI in publishing, ranging from peer review to manuscript quality control and journal matching, underscores its potential to streamline and enhance the entire research and publication process. Overall, while AI presents substantial benefits, ongoing research, and ethical guidelines are essential for its responsible integration into the evolving landscape of medical research and publishing.

The integration of AI in medical research has revolutionized efficiency and innovation, impacting data analysis, writing assistance, publishing, and others. While AI tools offer significant benefits, ethical considerations such as biases and content ownership must be addressed. Ongoing research and collaborative efforts are crucial to ensure responsible and transparent AI implementation in the dynamic landscape of medical research and publishing.

Background Evidence-based decision-making (EBDM) is essential in contemporary dentistry. However, navigating the extensive and constantly evolving scientific literature can be challenging. Large language models (LLMs), such as ChatGPT-4, have the potential to transform EBDM by analyzing vast datasets and extracting critical information, thereby significantly reducing the time required to find evidence. This observational feasibility study investigates ChatGPT-4's potential in dental EBDM, focusing on its capabilities, strengths, and limitations. Materials and methods In this observational feasibility study, two independent examiners conducted interactive sessions with ChatGPT-4. Five clinical scenarios were explored using the Google Chrome web browser, accessing publicly available scientific evidence from Cochrane, ADA, and PubMed. This approach ensured compliance with the Cochrane guidelines for EBDM. Two independent dentists engaged with ChatGPT-4 in simulated real-life clinical scenarios to seek scientific information. The output from ChatGPT-4 for each scenario was assessed based on predetermined criteria. Its responses were evaluated for accuracy, relevance, efficiency, actionability, and ethical considerations using the ChatGPT-4 Response Scoring System (CRSS) and the ChatGPT-4 Generative Ability Matrix (C-GAM). Results ChatGPT-4 demonstrated consistent performance across all five clinical scenarios, achieving a C-GAM score of 46.4% and a CRSS score of 12 out of 28. It effectively identified relevant sources of evidence and provided concise summaries, potentially saving valuable time and enhancing access to information. No significant differences in scores were found when the responses to all clinical scenarios were analyzed independently by the two researchers. However, a notable limitation was its inability to provide specific web links directing users to relevant scientific articles. Additionally, while ChatGPT-4 offered suggestions for incorporating the latest scientific publications into decision-making, it could not generate direct links to these articles. Conclusion Despite its current limitations, ChatGPT-4, as a generative AI, can assist clinicians in making evidence-based decisions. It can save time compared to conventional search engines. Ethical considerations must be prioritized in training these models to ensure that clinicians make responsible, evidence-based decisions rather than relying solely on specific evidence statements provided by ChatGPT-4. This model shows its potential as an AI tool for EBDM in dentistry. Further development and training could address existing limitations and enhance its effectiveness; however, clinicians must retain ultimate responsibility for informed decisions, necessitating expertise and critical evaluation of the evidence presented.

This study investigates the influence of structural empowerment and psychological capital on nurse work engagement within the context of rising healthcare demands and nursing staff shortages.

A cross-sectional descriptive study involving 778 registered nurses from six tertiary hospitals in Hangzhou, China, was conducted. Data were collected using multiple tools, including a demographic questionnaire, the CWEQ-II (Conditions for Work Effectiveness Questionnaire II), the PCQ (Psychological Capital Questionnaire), and the UWES-9 (Utrecht Work Engagement Scale-9). SPSS 27.0 was used for Pearson correlation and regression analyses, while structural equation modeling (SEM) in AMOS was employed to explore relationships among variables. Model fit was evaluated using chi-square, CFI, AGFI, and RMSEA indices.

Structural empowerment and psychological capital were significantly and positively correlated with nurses' work engagement. Regression analysis indicated that structural empowerment (support, resources, opportunity, and information) and psychological capital (optimism, resilience, self-efficacy, and hope) were significant positive predictors of work engagement (p < 0.01), jointly accounting for 69% of its variance. SEM analysis further revealed that structural empowerment indirectly influenced work engagement through psychological capital, with significant path coefficients (P < 0.001) and a good model fit (χ²/df = 3.727, P = 0.000, RMSEA = 0.059).

Structural empowerment and psychological capital are crucial factors in enhancing nurse work engagement, effectively supporting nurses' workplace performance. Management should focus on fostering psychological capital and enhancing structural empowerment to improve care quality and job satisfaction. This study provides empirical evidence for nursing management practice and suggests that future research should explore dynamic relationships among these variables in various populations and settings.

This study evaluated the effectiveness of large language models (LLMs), specifically ChatGPT 4o and a custom-designed model, Meta-Analysis Librarian, in generating accurate search strings for systematic reviews (SRs) in the field of anesthesiology.

We selected 85 SRs from the top 10 anesthesiology journals, according to Web of Science rankings, and extracted reference lists as benchmarks. Using study titles as input, we generated four search strings per SR: three with ChatGPT 4o using general prompts and one with the Meta-Analysis Librarian model, which follows a structured, Population, Intervention, Comparator, Outcome-based approach aligned with Cochrane Handbook standards. Each search string was used to query PubMed, and the retrieved results were compared with the PubMed retrieved studies from the original search string in each SR to assess retrieval accuracy. Statistical analysis compared the performance of each model.

Original search strings demonstrated superior performance with a 65% (IQR: 43%-81%) retrieval rate, which was statistically different from both LLM groups in PubMed retrieved studies (p=0.001). The Meta-Analysis Librarian achieved a superior median retrieval rate to ChatGPT 4o (median, (IQR); 24% (13%-38%) vs 6% (0%-14%), respectively).

The findings of this study highlight the significant advantage of using original search strings over LLM-generated search strings in PubMed retrieval studies. The Meta-Analysis Librarian demonstrated notable superiority in retrieval performance compared with ChatGPT 4o. Further research is needed to assess the broader applicability of LLM-generated search strings, especially across multiple databases.

Engaging in research during medical training is crucial for fostering critical thinking, enhancing clinical skills, and deepening understanding of medical science. Despite its importance, the shortage of physician-scientists lingers with many trainees and junior doctors encountering challenges navigating the research process. Drawing on current literature, this article provides a comprehensive roadmap, categorising 12 actionable strategies into five themes, to help medical trainees overcome common obstacles and optimise their research experience. The strategies include early planning, research conduct and integrity, productivity and time management, collaboration and dissemination, and personal growth and development. By implementing these evidence-based recommendations, derived from current literature and expert insights, medical trainees can refine their research skills, produce high-quality outputs, and contribute meaningfully to the scientific community, ultimately enriching their medical training and future careers.

Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. In parallel, generative AI, driven by large language models, is becoming a powerful tool for optimising the research workflow by supporting with content generation, literature review automation and clinical decision support. This review explores the current applications and future potential of both discriminative and generative AI in rheumatology. It also highlights the challenges posed by these technologies, such as ethical concerns and the need for rigorous validation and regulatory oversight. The integration of AI in rheumatology promises substantial advancements but requires a balanced approach to optimise benefits and minimise potential possible downsides.

Owing to the exceptional porous properties of metal-organic frameworks (MOFs), there has recently been a surge of interest, evidenced by a plethora of research into their design, synthesis, properties, and applications. This expanding research landscape has driven significant advancements in the precise regulation of MOF design and synthesis. Initially dominated by large-scale synthesis approaches, this field has evolved towards more targeted functional modifications. Recently, the integration of computational science, particularly through artificial intelligence predictions, has ushered in a new era of innovation, enabling more precise and efficient MOF design and synthesis methodologies. The objective of this review is to provide readers with an extensive overview of the development process of MOF design and synthesis, and to present visions for future developments.

Measuring adolescents' and youths' perception of their Cyber-Self can enhance the understanding of how digital technology influences identity formation. While psychological literature offers numerous measures of the self, there is a notable lack of studies addressing the measurement of the Cyber-Self. This study aims to evaluate the reliability, factorial- and criterion-related validity, and measurement invariance of the Cyber-Self Scale (CSS) across age and gender among the youth and adolescents in Saudi Arabia.

The Cyber Relationship Motives (CRM) and E-Emotional Questionnaire (EEQ) were administered to students at Umm Al-Qura University (N = 335), aged 17-31 years (39.7% male, 60.3% female; mean (M) = 21.75, standard deviation (SD) = 2.17).

The results indicated significant positive correlations between the sub-components of the CRM and EEQ. One item was selected based on two criteria: the highest correlation with other items and the highest correlation with the general factor. A total of 12 items were identified as the final form of the CSS, which demonstrated acceptable internal consistency for both male and female participants. Confirmatory factor analysis (CFA) revealed that the CSS model fit the data well, with all 12 items meeting the fit criteria for chi-square and root mean square error of approximation (RMSEA).

The Arabic version of the CSS is sufficiently reliable and valid for use among Arabic-speaking adolescents and youth. Further research is recommended to examine its measurement invariance over extended periods.

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.

To empower nursing graduate students, in master's or doctoral programs, through distance-accessible methods for conducting integrative reviews, enhancing their ability to transition from clinical to publication-oriented writing.

Mastering literature review methods is vital for advancing evidence-based practice. Integrative reviews, inclusive of multiple research methodologies, offer a comprehensive approach suited for nursing students. However, transitioning from clinical to publication-oriented writing poses challenges, necessitating innovative solutions.

This discussion presents strategic writing methods, including technologies for distance-accessible collaboration and data organization, framework use and critical analysis skills to support nursing graduate students in conducting integrative reviews.

Approaches for preparing and developing an integrative review are outlined, including integrating online tools and collaborative platforms and structured frameworks to guide data organization and critical analysis. Two published integrative reviews exemplifying these approaches are presented.

Distance-accessible and strategic approaches have significantly improved the quality of integrative literature reviews conducted by nursing graduate students. These innovations have equipped students with essential skills for navigating the contemporary academic landscape.

Embracing innovative approaches and staying informed about technological advancements empowers nursing graduate students to excel in their research pursuits. This contributes to evidence-based practice and nursing scholarship in the evolving healthcare landscape.

Artificial Intelligence (AI) is being used in every aspect of life today. It has found great application in the healthcare sector, with the use of this technology by medical schools all over the globe. AI has found multiple applications in medical fields such as diagnostics, medicine, surgery, oncology, radiology, ophthalmology, medical education, and numerous other medical fields. It has assisted in diagnosing conditions in a much quicker and more efficient manner, and the use of AI chatbots has greatly enhanced the learning process. Despite the benefits that AI applications provide, such as saving precious time for healthcare givers, there are also concerns regarding AI, mainly, ethical, and the fact that they might render the human race unemployed. However, despite these concerns, a lot of innovations are being made using AI applications, which show a very bright prospect for this technology. Although humans use AI in every part of their daily lives, they are also opposed to its use because they believe it could eventually replace them in the future. In this review of literature, a detailed analysis of the use of AI in the healthcare industry and medical education will be done, along with its shortcomings as well as its future prospects.

To evaluate GPT-4's performance in interpreting osteoarthritis (OA) treatment guidelines from the USA and China, and to assess its ability to diagnose and manage orthopaedic cases.

The study was conducted using publicly available OA treatment guidelines and simulated orthopaedic case scenarios.

No human participants were involved. The evaluation focused on GPT-4's responses to clinical guidelines and case questions, assessed by two orthopaedic specialists.

Primary outcomes included the accuracy and completeness of GPT-4's responses to guideline-based queries and case scenarios. Metrics included the correct match rate, completeness score and stratification of case responses into predefined tiers of correctness.

In interpreting the American Academy of Orthopaedic Surgeons and Chinese OA guidelines, GPT-4 achieved a correct match rate of 46.4% and complete agreement with all score-2 recommendations. The accuracy score for guideline interpretation was 4.3±1.6 (95% CI 3.9 to 4.7), and the completeness score was 2.8±0.6 (95% CI 2.5 to 3.1). For case-based questions, GPT-4 demonstrated high performance, with over 88% of responses rated as comprehensive.

GPT-4 demonstrates promising capabilities as an auxiliary tool in orthopaedic clinical practice and patient education, with high levels of accuracy and completeness in guideline interpretation and clinical case analysis. However, further validation is necessary to establish its utility in real-world clinical settings.

As artificial intelligence systems like large language models (LLM) and natural language processing advance, the need to evaluate their utility within medicine and medical education grows. As medical research publications continue to grow exponentially, AI systems offer valuable opportunities to condense and synthesize information, especially in underrepresented areas such as Sleep Medicine. The present study aims to compare summarization capacity between LLM generated summaries of sleep medicine research article abstracts, to summaries generated by Medical Student (humans) and to evaluate if the research content, and literary readability summarized is retained comparably.

A collection of three AI-generated and human-generated summaries of sleep medicine research article abstracts were shared with 19 study participants (medical students) attending a sleep medicine conference. Participants were blind as to which summary was human or LLM generated. After reading both human and AI-generated research summaries participants completed a 1-5 Likert scale survey on the readability of the extracted writings. Participants also answered article-specific multiple-choice questions evaluating their comprehension of the summaries, as a representation of the quality of content retained by the AI-generated summaries.

An independent sample t-test between the AI-generated and human-generated summaries comprehension by study participants revealed no significant difference between the Likert readability ratings (p = 0.702). A chi-squared test of proportions revealed no significant association (χ 2 = 1.485, p = 0.223), and a McNemar test revealed no significant association between summary type and the proportion of correct responses to the comprehension multiple choice questions (p = 0.289).

Some limitations in this study were a small number of participants and user bias. Participants attended at a sleep conference and study summaries were all from sleep medicine journals. Lastly the summaries did not include graphs, numbers, and pictures, and thus were limited in material extraction. While the present analysis did not demonstrate a significant difference among the readability and content quality between the AI and human-generated summaries, limitations in the present study indicate that more research is needed to objectively measure, and further define strengths and weaknesses of AI models in condensing medical literature into efficient and accurate summaries.

Background: The integration of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT-4, is transforming healthcare. ChatGPT's potential to assist in decision-making for complex cases, such as spinal metastasis treatment, is promising but widely untested. Especially in cancer patients who develop spinal metastases, precise and personalized treatment is essential. This study examines ChatGPT-4's performance in treatment planning for spinal metastasis cases compared to experienced spine surgeons. Materials and Methods: Five spine metastasis cases were randomly selected from recent literature. Consequently, five spine surgeons and ChatGPT-4 were tasked with providing treatment recommendations for each case in a standardized manner. Responses were analyzed for frequency distribution, agreement, and subjective rater opinions. Results: ChatGPT's treatment recommendations aligned with the majority of human raters in 73% of treatment choices, with moderate to substantial agreement on systemic therapy, pain management, and supportive care. However, ChatGPT's recommendations tended towards generalized statements, with raters noting its generalized answers. Agreement among raters improved in sensitivity analyses excluding ChatGPT, particularly for controversial areas like surgical intervention and palliative care. Conclusions: ChatGPT shows potential in aligning with experienced surgeons on certain treatment aspects of spinal metastasis. However, its generalized approach highlights limitations, suggesting that training with specific clinical guidelines could potentially enhance its utility in complex case management. Further studies are necessary to refine AI applications in personalized healthcare decision-making.

The medical literature guides ethical clinical care by providing information on medical innovations, clinical care, the history of medical advances, explanations for past mistakes and inspiration for future discoveries. Ethical authorship practices are thus imperative to preserving the integrity of medical publications and fulfilling our obligations to ethical patient care. Unethical authorship practices such as plagiarism, guest authorship, and ghost authorship are increasing and pose serious threats to the medical literature. The rise of artificial intelligence in assisting scholarly work poses particular concerns. Authors may face severe and career-changing penalties for engaging in unethical authorship.

ChatGPT is widely used for writing tasks, yet its effects on medical students' academic writing remain underexplored. This study aims to elucidate ChatGPT's impact on academic writing efficiency and quality among medical students, while also evaluating students' attitudes towards its use in academic writing.

We collected systematic reviews from 130 third-year medical students and administered a questionnaire to assess ChatGPT usage and student attitudes. Three independent reviewers graded the papers using EASE guidelines, and statistical analysis compared articles generated with or without ChatGPT assistance across various parameters, with rigorous quality control ensuring survey reliability and validity.

In this study, 33 students (25.8%) utilized ChatGPT for writing (ChatGPT group) and 95 (74.2%) did not (Control group). The ChatGPT group exhibited significantly higher daily technology use and prior experience with ChatGPT (p < 0.05). Writing time was significantly reduced in the ChatGPT group (p = 0.04), with 69.7% completing tasks within 2-3 days compared to 48.4% in the control group. They also achieved higher article quality scores (p < 0.0001) with improvements in completeness, credibility, and scientific content. Self-assessment indicated enhanced writing skills (p < 0.01), confidence (p < 0.001), satisfaction (p < 0.001) and a positive attitude toward its future use in the ChatGPT group.

Integrating ChatGPT in medical academic writing, with proper guidance, improves efficiency and quality, illustrating artificial intelligence's potential in shaping medical education methodologies.

The declaration of the COVID-19 pandemic by the World Health Organization (WHO) in March 2020 has triggered the publication of thousands of papers covering a plethora of aspects of the pandemic, from epidemiology models [...].

The current study explored the influence of Chat Generative Pre-Trained Transformer (ChatGPT) on the concepts, parameters, policies, and practices of creativity and plagiarism in academic and research writing.

Data were collected from 10 researchers from 10 different countries (Australia, China, the UK, Brazil, Pakistan, Bangladesh, Iran, Nigeria, Trinidad and Tobago, and Turkiye) using semi-structured interviews. NVivo was employed for data analysis.

Based on the responses, five themes about the influence of ChatGPT on academic and research writing were generated, i.e., opportunity, human assistance, thought-provoking, time-saving, and negative attitude. Although the researchers were mostly positive about it, some feared it would degrade their writing skills and lead to plagiarism. Many of them believed that ChatGPT would redefine the concepts, parameters, and practices of creativity and plagiarism.

Creativity may no longer be restricted to the ability to write, but also to use ChatGPT or other large language models (LLMs) to write creatively. Some suggested that machine-generated text might be accepted as the new norm; however, using it without proper acknowledgment would be considered plagiarism. The researchers recommended allowing ChatGPT for academic and research writing; however, they strongly advised it to be regulated with limited use and proper acknowledgment.

At the intersection of artificial intelligence (AI) and healthcare, it is essential that clinicians grasp the ability of chatbots. AI-powered chatbots such as ChatGPT are being explored for their potential benefits by both individuals and institutions. The utility of ChatGPT (OpenAI) in various scenarios was explored through a series of recorded prompts and responses. In the clinical aspects, the chatbot facilitated tasks such as triage, patient consultation, diagnosis, and administrative responsibilities. Their capacity to translate and simplify intricate medical topics was also evaluated. For research purposes, the chatbots' abilities to suggest ideas, prepare protocols, assist in manuscript writing, guide statistical analyses, and recommend suitable journals were assessed. In the educational domain, chatbots were tested for simplifying complex subjects, reviewing procedural steps, generating clinical scenarios, and formulating multiple-choice questions. A comprehensive literature review was also conducted across Medline, Embase, and Web of Science. Chatbots, when optimally employed, can serve as invaluable resources in healthcare, spanning clinical, research, and educational domains. Their potential lies in enhancing efficiency, guiding decision-making, and facilitating patient care and education. However, their application requires a nuanced understanding and caution regarding their limitations.

Traditional medical artificial intelligence models that are approved for clinical use restrict themselves to single-modal data ( e.g ., images only), limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal transformer models in health care can effectively process and interpret diverse data forms, such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks, like United States Medical Licensing Examination question banks, and continue to improve with scale. However, the adoption of these advanced artificial intelligence models is not without challenges. While multimodal deep learning models like transformers offer promising advancements in health care, their integration requires careful consideration of the accompanying ethical and environmental challenges.

This study aimed to assess the ability of an artificial intelligence (AI)-based chatbot to generate abstracts from academic psychiatric articles. We provided 30 full-text psychiatric papers to ChatPDF (based on ChatGPT) and prompted generating a similar style structured or unstructured abstract. We further used 10 papers from Psychiatry Research as active comparators (unstructured format). We compared the quality of the ChatPDF-generated abstracts with the original human-written abstracts and examined the similarity, plagiarism, detected AI-content, and correctness of the AI-generated abstracts. Five experts evaluated the quality of the abstracts using a blinded approach. They also identified the abstracts written by the original authors and validated the conclusions produced by ChatPDF. We found that the similarity and plagiarism were relatively low (only 14.07% and 8.34%, respectively). The detected AI-content was 31.48% for generated structure-abstracts, 75.58% for unstructured-abstracts, and 66.48% for active comparators abstracts. For quality, generated structured-abstracts were rated similarly to originals, but unstructured ones received significantly lower scores. Experts rated 40% accuracy with structured abstracts, 73% with unstructured ones, and 77% for active comparators. However, 30% of AI-generated abstract conclusions were incorrect. In conclusion, the data organization capabilities of AI language models hold significant potential for applications to summarize information in clinical psychiatry. However, the use of ChatPDF to summarize psychiatric papers requires caution concerning accuracy.

Effective patient counseling in Obstetrics and gynecology is vital. Existing language barriers between Spanish-speaking patients and English-speaking providers may negatively impact patient understanding and adherence to medical recommendations, as language discordance between provider and patient has been associated with medication noncompliance, adverse drug events, and underuse of preventative care. Artificial intelligence large language models may be a helpful adjunct to patient care by generating counseling templates in Spanish.

The primary objective was to determine if large language models can generate proficient counseling templates in Spanish on obstetric and gynecology topics. Secondary objectives were to (1) compare the content, quality, and comprehensiveness of generated templates between different large language models, (2) compare the proficiency ratings among the large language model generated templates, and (3) assess which generated templates had potential for integration into clinical practice.

Cross-sectional study using free open-access large language models to generate counseling templates in Spanish on select obstetrics and gynecology topics. Native Spanish-speaking practicing obstetricians and gynecologists, who were blinded to the source large language model for each template, reviewed and subjectively scored each template on its content, quality, and comprehensiveness and considered it for integration into clinical practice. Proficiency ratings were calculated as a composite score of content, quality, and comprehensiveness. A score of >4 was considered proficient. Basic inferential statistics were performed.

All artificial intelligence large language models generated proficient obstetrics and gynecology counseling templates in Spanish, with Google Bard generating the most proficient template (p<0.0001) and outperforming the others in comprehensiveness (P=.03), quality (P=.04), and content (P=.01). Microsoft Bing received the lowest scores in these domains. Physicians were likely to be willing to incorporate the templates into clinical practice, with no significant discrepancy in the likelihood of integration based on the source large language model (P=.45).

Large language models have potential to generate proficient obstetrics and gynecology counseling templates in Spanish, which physicians would integrate into their clinical practice. Google Bard scored the highest across all attributes. There is an opportunity to use large language models to try to mitigate the language barriers in health care. Future studies should assess patient satisfaction, understanding, and adherence to clinical plans following receipt of these counseling templates.

Burn injuries often require immediate assistance and specialized care for optimal management and outcomes. The emergence of accessible artificial intelligence technology has just recently started being applied to healthcare decision making and patient education. However, its role in clinical recommendations is still under scrutiny. This study aims to evaluate ChatGPT's outputs and the appropriateness of its responses to commonly asked questions regarding acute burn care when compared to the American Burn Association Guidelines. Twelve commonly asked questions were formulated by a fellowship-trained burn surgeon to address the American Burn Association's recommendations on burn injuries, management, and patient referral. These questions were prompted into ChatGPT, and each response was compared with the aforementioned guidelines, the gold standard for accurate and evidence-based burn care recommendations. Three burn surgeons independently evaluated the appropriateness and comprehensiveness of each ChatGPT response based on the guidelines according to the modified Global Quality Score scale. The average score for ChatGPT-generated responses was 4.56 ± 0.65, indicating the responses were exceptional quality with the most important topics covered and in high concordance with the guidelines. This initial comparison of ChatGPT-generated responses and the American Burn Association guidelines demonstrates that ChatGPT can accurately and comprehensibly describe appropriate treatment and management plans for acute burn injuries. We foresee that ChatGPT may play a role as a complementary tool in medical decision making and patient education, having a profound impact on clinical practice, research, and education.

Artificial intelligence (AI) and machine learning (ML) are important tools across many fields of health and medical research. Pharmacoepidemiologists can bring essential methodological rigor and study design expertise to the design and use of these technologies within healthcare settings. AI/ML-based tools also play a role in pharmacoepidemiology research, as we may apply them to answer our own research questions, take responsibility for evaluating medical devices with AI/ML components, or participate in interdisciplinary research to create new AI/ML algorithms. While epidemiologic expertise is essential to deploying AI/ML responsibly and ethically, the rapid advancement of these technologies in the past decade has resulted in a knowledge gap for many in the field. This article provides a brief overview of core AI/ML concepts, followed by a discussion of potential applications of AI/ML in pharmacoepidemiology research, and closes with a review of important concepts across application areas, including interpretability and fairness. This review is intended to provide an accessible, practical overview of AI/ML for pharmacoepidemiology research, with references to further, more detailed resources on fundamental topics.

With the advancement of Artificial Intelligence (AI), it has had a profound impact on medical education. Understanding the advantages and issues of AI in medical education, providing guidance for educators, and overcoming challenges in the implementation process is particularly important.

The objective of this study is to explore the current state of AI applications in medical education.

A systematic search was conducted across databases such as PsycINFO, CINAHL, Scopus, PubMed, and Web of Science to identify relevant studies. The Critical Appraisal Skills Programme (CASP) was employed for the quality assessment of these studies, followed by thematic synthesis to analyze the themes from the included research.

Ultimately, 21 studies were identified, establishing four themes: (1) Shaping the Future: Current Trends in AI within Medical Education; (2) Advancing Medical Instruction: The Transformative Power of AI; (3) Navigating the Ethical Landscape of AI in Medical Education; (4) Fostering Synergy: Integrating Artificial Intelligence in Medical Curriculum.

Artificial intelligence's role in medical education, while not yet extensive, is impactful and promising. Despite challenges, including ethical concerns over privacy, responsibility, and humanistic care, future efforts should focus on integrating AI through targeted courses to improve educational quality.

The diversities of medical information resources and health information needs in recent years have caused another form of summarization and abstract writing to appear in the information capsule (IC) versus various types of abstracts. The present study was conducted with the aim to analyze the current IC, giving a unique definition and a standard structure for the development of an IC, and how it can be represented and implemented.

This study was conducted in three phases in the form of a qualitative study. In the first phase, a library review study was done on the relevant websites and international databases, such as PubMed, Science Direct, Web of Sciences, Google Scholar, ProQuest, and Embase. In the second phase, the results of the previous stage were discussed with a panel of experts. In the third phase, a suggested frame for an IC was stated.

A specific structure was suggested for IC so that, in addition to having parts of similar cases, they had extra parts. The suggested frame includes title, name of the IC writers and reviewers, question or goals, design or methods, setting, patient or community of the study, result, commentary, citation, topic, picture, and tag and can be used in different fields.

Due to the importance of IC in summarizing information, our suggested structure should be used in other fields and be subject to trial and error.

The large-scale language model, GPT-4-1106-preview, supports text of up to 128 k characters, which has enhanced the capability of processing vast quantities of text. This model can perform efficient and accurate text data mining without the need for retraining, aided by prompt engineering.

The research approach includes prompt engineering and text vectorization processing. In this study, prompt engineering is applied to assist ChatGPT in text mining. Subsequently, the mined results are vectorized and incorporated into a local knowledge base. After cleansing 306 medical papers, data extraction was performed using ChatGPT. Following a validation and filtering process, 241 medical case data entries were obtained, leading to the construction of a local medical knowledge base. Additionally, drawing upon the Langchain framework and utilizing the local knowledge base in conjunction with ChatGPT, we successfully developed a fast and reliable chatbot. This chatbot is capable of providing recommended diagnostic and treatment information for various diseases.

The performance of the designed ChatGPT model, which was enhanced by data from the local knowledge base, exceeded that of the original model by 7.90% on a set of medical questions.

ChatGPT, assisted by prompt engineering, demonstrates effective data mining capabilities for large-scale medical texts. In the future, we plan to incorporate a richer array of medical case data, expand the scale of the knowledge base, and enhance ChatGPT's performance in the medical field.

Background: This study aimed to assess the reproducibility and reliability of Chat-Based GPT (ChatGPT)'s responses to 19 statements regarding the management of hip fractures in older adults as adopted by the American Academy of Orthopaedic Surgeons' (AAOS) evidence-based clinical practice guidelines. Methods: Nineteen statements were obtained from the 2021 AAOS evidence-based clinical practice guidelines. After generating questions based on these 19 statements, we set a prompt for both the GPT-4o and GPT-4 models. We repeated this process three times at 24 h intervals for both models, producing outputs A, B, and C. ChatGPT's performance, the intra-ChatGPT reliability, and the accuracy rates were assessed to evaluate the reproducibility and reliability of the hip fracture-related guidelines. Regarding the strengths of the recommendation compared with the 2021 AAOS guidelines, we observed accuracy of 0.684, 0.579, and 0.632 for outputs A, B, and C, respectively. Results: The precision was 0.740, 0.737, and 0.718 in outputs A, B, and C, respectively. For the reliability of the strengths of the recommendation, the Fleiss kappa was 0.409, indicating a moderate level of agreement. No statistical differences in the strengths of the recommendation were observed in outputs A, B, and C between the GPT-4o and GPT-4 versions. Conclusion: ChatGPT may be useful in providing guidelines for hip fractures but performs poorly in terms of accuracy and precision. However, hallucinations remain an unresolved limitation associated with using ChatGPT to search for hip fracture guidelines. The effective utilization of ChatGPT as a patient education tool for the management of hip fractures should be addressed in the future.

The increasing development of artificial intelligence (AI) generative models in otolaryngology-head and neck surgery will progressively change our practice. Practitioners and patients have access to AI resources, improving information, knowledge, and practice of patient care. This article summarizes the currently investigated applications of AI generative models, particularly Chatbot Generative Pre-trained Transformer, in otolaryngology-head and neck surgery.

Large language models (LLMs) are a unique form of machine learning that facilitates inputs of unstructured text/numerical information for meaningful interpretation and prediction. Recently, LLMs have become commercialized, allowing the average person to access these incredibly powerful tools. Early adopters focused on LLM use in performing logical tasks, including-but not limited to-generating titles, identifying key words, summarizing text, initial editing of scientific work, improving statistical protocols, and performing statistical analysis. More recently, LLMs have been expanded to clinical practice and academia to perform higher cognitive and creative tasks. LLMs provide personalized assistance in learning, facilitate the management of electronic medical records, and offer valuable insights into clinical decision making in cardiology. They enhance patient education by explaining intricate medical conditions in lay terms, have a vast library of knowledge to help clinicians expedite administrative tasks, provide useful feedback regarding content of scientific writing, and assist in the peer-review process. Despite their impressive capabilities, LLMs are not without limitations. They are susceptible to generating incorrect or plagiarized content, face challenges in handling tasks without detailed prompts, and lack originality. These limitations underscore the importance of human oversight in using LLMs in medical science and clinical practice. As LLMs continue to evolve, addressing these challenges will be crucial in maximizing their potential benefits while mitigating risks. This review explores the functions, opportunities, and constraints of LLMs, with a focus on their impact on cardiology, illustrating both the transformative power and the boundaries of current technology in medicine.

OpenAI's ChatGPT (Generative Pre-trained Transformer) is a chatbot that answers questions and performs writing tasks in a conversational tone. Within months of release, multiple sectors are contemplating the varied applications of this chatbot, including medicine, education, and research, all of which are involved in medical communication and scientific publishing. Medical writers and academics use several artificial intelligence (AI) tools and software for research, literature survey, data analyses, referencing, and writing. There are benefits of using different AI tools in medical writing. However, using chatbots for medical communications pose some major concerns such as potential inaccuracies, data bias, security, and ethical issues. Perceived incorrect notions also limit their use. Moreover, ChatGPT can also be challenging if used incorrectly and for irrelevant tasks. If used appropriately, ChatGPT will not only upgrade the knowledge of the medical writer but also save time and energy that could be directed toward more creative and analytical areas requiring expert skill sets. This review introduces chatbots, outlines the progress in ChatGPT research, elaborates the potential uses of ChatGPT in medical communications along with its challenges and limitations, and proposes future research perspectives. It aims to provide guidance for doctors, researchers, and medical writers on the uses of ChatGPT in medical communications.

Artificial intelligence (AI), particularly computer vision and large language models, will impact facial plastic and reconstructive surgery (FPRS) by enhancing diagnostic accuracy, refining surgical planning, and improving post-operative evaluations. These advancements can address subjective limitations of aesthetic surgery by providing objective tools for patient evaluation. Despite these advancements, AI in FPRS has yet to be fully integrated in the clinic setting and faces numerous challenges including algorithmic bias, ethical considerations, and need for validation. This article discusses current and emerging AI technologies in FPRS for the clinic setting, providing a glimpse of its future potential.

The objective is to assess the precision of references generated by Chatbot Generative Pretrained Transformer version 4 (ChatGPT-4) in scientific literature pertaining to colon and rectal surgery.

Ten frequently studied keywords pertaining to colon and rectal surgery were chosen: colon cancer, rectal cancer, anal cancer, total neoadjuvant therapy, diverticulitis, low anterior resection, transanal minimally invasive surgery, ileal pouch anal anastomosis, abdominoperineal resection, and hemorrhoidectomy. ChatGPT-4 was prompted to search for the most representative citations for all keywords. After this, two separate evaluators meticulously examined the outcomes each key element, awarding full accuracy to generated citations in which there was no discrepancies in any of the fields when cross-referenced with the Scopus, Google, and PubMed databases. References from ChatGPT-4 underwent a thorough review process, which involved careful examination of key elements such as the article title, authors, journal name, publication year, and Digital Object Identifier (DOI).

Forty-one of the 100 references generated by were fully accurate; however, but none included a DOI. Partial accuracy was observed in 67 of the references, which were identifiable by title and journal. Performance varied across specific keywords; for example, references for colon and rectal cancer were 100% identifiable by title and journal, but no term had 100% accuracy across all categories. Notably, none of the generated references correctly listed all authors. Conducted within a short timeframe during which ChatGPT4 is rapidly evolving and updating its knowledge base.

While ChatGPT-4 offers improvements over its predecessors and shows potential for use in academic literature, its inconsistent performance across categories, lack of DOIs, and irregularities in authorship listings raise concerns about its readiness for application in the field of colon and rectal surgery research.

The attainment of academic superiority relies heavily upon the accessibility of scholarly resources and the expression of research findings through faultless language usage. Although modern tools, such as the Publish or Perish software program, are proficient in sourcing academic papers based on specific keywords, they often fall short of extracting comprehensive content, including crucial references. The challenge of linguistic precision remains a prominent issue, particularly for research papers composed by non-native English speakers who may encounter word usage errors. This manuscript serves a twofold purpose: firstly, it reassesses the effectiveness of ChatGPT-4 in the context of retrieving pertinent references tailored to specific research topics. Secondly, it introduces a suite of language editing services that are skilled in rectifying word usage errors, ensuring the refined presentation of research outcomes. The article also provides practical guidelines for formulating precise queries to mitigate the risks of erroneous language usage and the inclusion of spurious references. In the ever-evolving realm of academic discourse, leveraging the potential of advanced AI, such as ChatGPT-4, can significantly enhance the quality and impact of scientific publications.

This commentary explores the utilization of generative artificial intelligence (AI), particularly Google Gemini (previously Bard), in enhancing English presentation skills among non-native researchers. We present a step-by-step methodology for using Google Gemini's Speech-to-Text and Text-to-Speech features. Our findings suggest that Google Gemini effectively aids in draft presentations, pronunciation practice, and content verification, tapping into an area often unexplored-using AI for presentation skills in scientific research. Despite its potential, users must exercise caution due to the experimental nature of this AI technology. Adapting to such technologies is timely and beneficial for the global scientific community.

Objectives First reports suggest that artificial intelligence (AI) such as ChatGPT-4 (Open AI, ChatGPT-4, San Francisco, USA) might represent reliable tools for therapeutic decisions in some medical conditions. This study aims to assess the decisional capacity of ChatGPT-4 in patients with head and neck carcinomas, using the multidisciplinary oncology meeting (MOM) and the National Comprehensive Cancer Network (NCCN) decision as references. Methods This retrospective study included 263 patients with squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx, and larynx who were followed at our institution between January 1, 2016, and December 31, 2021. The recommendation of GPT4 for the first- and second-line treatments was compared to the MOM decision and NCCN guidelines. The degrees of agreement were calculated using the Kappa method, which measures the degree of agreement between two evaluators. Results ChatGPT-4 demonstrated a moderate agreement in first-line treatment recommendations (Kappa = 0.48) and a substantial agreement (Kappa = 0.78) in second-line treatment recommendations compared to the decisions from MOM. A substantial agreement with the NCCN guidelines for both first- and second-line treatments was observed (Kappa = 0.72 and 0.66, respectively). The degree of agreement decreased when the decision included gastrostomy, patients over 70, and those with comorbidities. Conclusions The study illustrates that while ChatGPT-4 can significantly support clinical decision-making in oncology by aligning closely with expert recommendations and established guidelines, ongoing enhancements and training are crucial. The findings advocate for the continued evolution of AI tools to better handle the nuanced aspects of patient health profiles, thus broadening their applicability and reliability in clinical practice.

To review the current literature on the application, accuracy, and performance of Chatbot Generative Pre-Trained Transformer (ChatGPT) in Otolaryngology-Head and Neck Surgery.

PubMED, Cochrane Library, and Scopus.

A comprehensive review of the literature on the applications of ChatGPT in otolaryngology was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses statement.

ChatGPT provides imperfect patient information or general knowledge related to diseases found in Otolaryngology-Head and Neck Surgery. In clinical practice, despite suboptimal performance, studies reported that the model is more accurate in providing diagnoses, than in suggesting the most adequate additional examinations and treatments related to clinical vignettes or real clinical cases. ChatGPT has been used as an adjunct tool to improve scientific reports (referencing, spelling correction), to elaborate study protocols, or to take student or resident exams reporting several levels of accuracy. The stability of ChatGPT responses throughout repeated questions appeared high but many studies reported some hallucination events, particularly in providing scientific references.

To date, most applications of ChatGPT are limited in generating disease or treatment information, and in the improvement of the management of clinical cases. The lack of comparison of ChatGPT performance with other large language models is the main limitation of the current research. Its ability to analyze clinical images has not yet been investigated in otolaryngology although upper airway tract or ear images are an important step in the diagnosis of most common ear, nose, and throat conditions. This review may help otolaryngologists to conceive new applications in further research.

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community's understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.