A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.

Every drug development is a complex and long journey. Clinical pharmacology is an essential discipline in modern drug development. With its applications, computational modelling, and simulation techniques, it can significantly contribute to the efficiency in drug development today. In this perspective, we highlight why pharmacokinetics and pharmacodynamics are important, what developers need to consider in their clinical development programme, how modelling influences the development process, and discuss recent trends such as artificial intelligence and machine learning that have the potential to reshape future drug development.

With the advent of artificial intelligence (AI), novel opportunities arise to revolutionize healthcare delivery and improve population health. This review provides a state-of-the-art overview of recent advancements in AI technologies and their applications in enhancing cardiovascular health at the population level. From predictive analytics to personalized interventions, AI-driven approaches are increasingly being utilized to analyse vast amounts of healthcare data, uncover disease patterns, and optimize resource allocation. Furthermore, AI-enabled technologies such as wearable devices and remote monitoring systems facilitate continuous cardiac monitoring, early detection of diseases, and promise more timely interventions. Additionally, AI-powered systems aid healthcare professionals in clinical decision-making processes, thereby improving accuracy and treatment effectiveness. By using AI systems to augment existing data sources, such as registries and biobanks, completely new research questions can be addressed to identify novel mechanisms and pharmaceutical targets. Despite this remarkable potential of AI in enhancing population health, challenges related to legal issues, data privacy, algorithm bias, and ethical considerations must be addressed to ensure equitable access and improved outcomes for all individuals.

This article presents a cost-effective, modular infusion platform to help diabetes specialists customize and understand infusion pump mechanics and control principles. Traditional insulin pumps are costly and inflexible, limiting accessibility, and particularly in low-resource settings. Inspired by open-source initiatives like OpenAPS, this platform engages specialists in device operation and customization, offering practical insights into infusion technology.

An initial survey assessed technological literacy, customization interests, and feature preferences among Mexican diabetes specialists, followed by a hands-on engagement session with the platform's hardware. Core components are described and chosen for reliability, affordability, and integration ease. A follow-up survey evaluated specialists' confidence and interest in device customization, gathering feedback on usability and design.

Survey data showed strong specialist interest in understanding device mechanics and high confidence in customization after hands-on engagement. Most specialists found the hardware layout conducive to experimentation, with significant interest in closed-loop capabilities. Key valued features included safety, affordability, ease of use, customization, and integration of diverse continuous glucose monitors, with added suggestions for potential clinical certification, cost-effective supplies, and artificial intelligence integration.

This platform offers a promising educational and developmental tool in diabetes management, bridging clinical application, and customization. Its low-cost, modular design provides a feasible solution for low-resource settings, equipping specialists to tailor devices for specific patient needs. While the platform's educational potential is clear, further studies and validation are essential for a possible transition to a clinical-grade device. Continued development could democratize access to advanced diabetes technology, transforming specialist training, and patient care.

Despite notable advancements in artificial intelligence (AI) that enable complex systems to perform certain tasks more accurately than medical experts, the impact on patient-relevant outcomes remains uncertain. To address this gap, this systematic review assesses the benefits and harms associated with AI-related algorithmic decision-making (ADM) systems used by healthcare professionals, compared to standard care.

In accordance with the PRISMA guidelines, we included interventional and observational studies published as peer-reviewed full-text articles that met the following criteria: human patients; interventions involving algorithmic decision-making systems, developed with and/or utilizing machine learning (ML); and outcomes describing patient-relevant benefits and harms that directly affect health and quality of life, such as mortality and morbidity. Studies that did not undergo preregistration, lacked a standard-of-care control, or pertained to systems that assist in the execution of actions (e.g., in robotics) were excluded. We searched MEDLINE, EMBASE, IEEE Xplore, and Google Scholar for studies published in the past decade up to 31 March 2024. We assessed risk of bias using Cochrane's RoB 2 and ROBINS-I tools, and reporting transparency with CONSORT-AI and TRIPOD-AI. Two researchers independently managed the processes and resolved conflicts through discussion. This review has been registered with PROSPERO (CRD42023412156) and the study protocol has been published.

Out of 2,582 records identified after deduplication, 18 randomized controlled trials (RCTs) and one cohort study met the inclusion criteria, covering specialties such as psychiatry, oncology, and internal medicine. Collectively, the studies included a median of 243 patients (IQR 124-828), with a median of 50.5% female participants (range 12.5-79.0, IQR 43.6-53.6) across intervention and control groups. Four studies were classified as having low risk of bias, seven showed some concerns, and another seven were assessed as having high or serious risk of bias. Reporting transparency varied considerably: six studies showed high compliance, four moderate, and five low compliance with CONSORT-AI or TRIPOD-AI. Twelve studies (63%) reported patient-relevant benefits. Of those with low risk of bias, interventions reduced length of stay in hospital and intensive care unit (10.3 vs. 13.0 days, p = 0.042; 6.3 vs. 8.4 days, p = 0.030), in-hospital mortality (9.0% vs. 21.3%, p = 0.018), and depression symptoms in non-complex cases (45.1% vs. 52.3%, p = 0.03). However, harms were frequently underreported, with only eight studies (42%) documenting adverse events. No study reported an increase in adverse events as a result of the interventions.

The current evidence on AI-related ADM systems provides limited insights into patient-relevant outcomes. Our findings underscore the essential need for rigorous evaluations of clinical benefits, reinforced compliance with methodological standards, and balanced consideration of both benefits and harms to ensure meaningful integration into healthcare practice.

This study did not receive any funding.

Pregnancies impacted by diabetes face the compounded challenge of strict glycemic control with mounting insulin resistance as the pregnancy progresses. New technological advances, including artificial intelligence (AI) and the Internet of Medical Things (IoMT), are revolutionizing health care delivery by providing innovative solutions for diabetes care during pregnancy. Together, AI and the IoMT are a multibillion-dollar industry that integrates advanced medical devices and sensors into a connected network that enables continuous monitoring of glucose levels. AI-driven clinical decision support systems (CDSSs) can predict glucose trends and provide tailored evidence-based treatments with real-time adjustments as insulin resistance changes with placental growth. Additionally, mobile health (mHealth) applications facilitate patient education and self-management through real-time tracking of diet, physical activity, and glucose levels. Remote monitoring capabilities are particularly beneficial for pregnant persons with diabetes as they extend quality care to underserved populations and reduce the need for frequent in-person visits. This high-resolution monitoring allows physicians and patients access to an unprecedented wealth of data to make more informed decisions based on real-time data, reducing complications for both the mother and fetus. These technologies can potentially improve maternal and fetal outcomes by enabling timely, individualized interventions based on personalized health data. While AI and IoMT offer significant promise in enhancing diabetes care for improved maternal and fetal outcomes, their implementation must address challenges such as data security, cost-effectiveness, and preserving the essential patient-provider relationship. KEY POINTS: · The IoMT expands how patients interact with their health care.. · AI has widespread application in the care of pregnancies complicated by diabetes.. · A need for validation and black-box methodologies challenges the application of AI-based tools.. · As research in AI grows, considerations for data privacy and ethical dilemmas will be required..

Considerable efforts to standardize continuous glucose monitoring (CGM) have occurred in recent years. The aim was to perform an analysis of clinical studies in clinicaltrials.gov to evaluate trends in CGM endpoint adoption.

Clinicaltrials.gov was searched for studies of drugs, devices and combination products containing CGM terms posted from 2012 to 2023. 1269 studies were returned and 954 were excluded. 315 studies were divided into two periods (P1 [2012-2017] and P2 [2018-2023]) and differences analyzed using descriptive statistics and two-tailed t tests.

There was a significant 60.3% increase in total clinical studies from P1 (121) to P2 (194). Phase 2 and Phase 3 Studies both saw significant increases of 125.8 and 169.2%, respectively, in P2. Adult-only studies predominated in both periods, with a 40.4% increase in P2. Studies that included pediatric populations, although smaller in number, increased significantly. Most studies were nonindustry-funded, and studies in this category saw a significant 80.0% increase in P2. However, industry-only funded studies also increased significantly by 78.4% in P2 in the same period. Studies of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) increased by 55.8% and 26.9%, respectively, but increases were not statistically significant. Studies of nondiabetes-related indications did increase significantly (233.3%). 27.6% of studies used CGM-derived metrics as primary endpoints (PE). Studies that used time in range (TIR) increased by 222.4% in P2, which was significant. Conversely studies that used mean amplitude of glycemic excursions (MAGE) decreased significantly by 71.3%.

Our data provide evidence of significant increases in the application of CGM endpoints in clinical studies in the last six years, including studies with TIR as the PE. Increases have been driven largely by academia, but our data show that industry is starting to follow suit. The significant increase in studies that included pediatrics is encouraging.

Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience.

This review aimed to map the use cases of artificial intelligence (AI) in NIBGM.

A systematic scoping review was conducted according to the Arksey O'Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 independent reviewers, descriptive analysis was conducted, and findings were presented narratively. Study characteristics (author, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems) were extracted independently and cross-checked by 2 investigators. Methodological quality appraisal was conducted using the Checklist for assessment of medical AI.

A total of 33 papers were included, representing studies from Asia, the United States, Europe, the Middle East, and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 58%) to estimate blood glucose levels (n=27, 82%). Others used electrochemical sensors (n=4), imaging (n=2), mixed techniques (n=2), and tissue impedance (n=1). Accuracy ranged from 35.56% to 94.23% and Clarke error grid (A+B) ranged from 86.91% to 100%. The most popular machine learning algorithm used was random forest (n=10) and the most popular deep learning model was the artificial neural network (n=6). The mean overall checklist for assessment of medical AI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy was wide due to the heterogeneity of models and input data.

Efforts are needed to standardize and regulate the use of AI technologies in BGM, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems. The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management.

Digital decision support and remote patient monitoring may improve outcomes and efficiency, but rarely scale beyond a single institution. Over the last 5 years, the platform Timely Interventions for Diabetes Excellence (TIDE) has been associated with reduced care provider screen time and improved, equitable type 1 diabetes care and outcomes for 268 patients in a heterogeneous population as part of the Teamwork, Targets, Technology, and Tight Control (4T) Study (NCT03968055, NCT04336969). Previous efforts to deploy TIDE at other institutions continue to face delays. In partnership with the diabetes technology non-profit, Tidepool, we developed Tidepool-TIDE, a clinic-agnostic, turnkey solution available to any clinic in the United States. We present how we overcame common technical and operational barriers specific to scaling digital health technology from one site to many. The concepts described are broadly applicable for institutions interested in facilitating broader adoption of digital technology for population-level management of chronic health conditions.

Insulin pumps and other smart devices have recently made significant advancements in the treatment of diabetes, a disorder that affects people all over the world. The development of medical AI has been influenced by AI methods designed to help physicians make diagnoses, choose a course of therapy, and predict outcomes. In this article, we thoroughly analyse how AI is being used to enhance and personalize diabetes treatment. The search turned up 77 original research papers, from which we've selected the most crucial information regarding the learning models employed, the data typology, the deployment stage, and the application domains. We identified two key trends, enabled mostly by AI: patient-based therapy personalization and therapeutic algorithm optimization. In the meanwhile, we point out various shortcomings in the existing literature, like a lack of multimodal database analysis or a lack of interpretability. The rapid improvements in AI and the expansion of the amount of data already available offer the possibility to overcome these difficulties shortly and enable a wider deployment of this technology in clinical settings.

In this commentary, we elucidate three indispensable evaluation steps toward the real-world deployment of machine learning within the healthcare sector and demonstrate referable examples for diagnostic, therapeutic, and prognostic tasks. We encourage researchers to move beyond retrospective and within-sample validation, and step into the practical implementation at the bedside rather than leaving developed machine learning models in the dust of archived literature.

We propose an artificial-pancreas-like algorithm (AP-A) that could automatically determine the preprandial insulin dose based on intermittently scanned continuous glucose monitoring (isCGM) data trajectories in multiple dose injection (MDI) therapy.

We aim to determine whether preprandial insulin dose adjustments guided by the AP-A are as effective and safe as physician decisions.

We performed a randomized, single-blind, clinical trial at a tertiary, referral hospital in Beijing, China. Type 2 diabetes participants were eligible if they were aged 18 years or older, with a glycated hemoglobin A1c of 8.0% or higher. Eligible participants were randomly assigned (1:1) to the AP-A arm supervised by physician and the conventional physician treatment arm. The primary objective was to compare percentage time spent with sensor glucose level in 3.9 to 10.0 mmol/L (TIR) between the 2 study arms. Safety was assessed by the percentage time spent with sensor glucose level below 3.0 mmol/L (TBR).

A total of 140 participants were screened, of whom 119 were randomly assigned to the AP-A arm (n = 59) or physician arm (n = 60). The TIR achieved by the AP-A arm was statistically noninferior compared with the control arm (72.4% [63.3%-82.1%] vs 71.2% [54.9%-81.4%]), with a median difference of 1.33% (95% CI, -6.00 to 10.94, noninferiority margin -7.5%). TBR was also statistically noninferior between the AP-A and control arms (0.0% [0.0%-0.0%] vs 0.0% [0.0%-0.0%]), respectively; median difference (95% CI, 0.00% [0.00%-0.00%], noninferiority margin 2.0%).

The AP-A-supported physician titration of preprandial insulin dosage offers noninferior glycemic control compared with optimal physician care in type 2 diabetes.

This study aimed to develop and validate distinct nomogram models for assessing CVD risk in individuals with prediabetes and diabetes. In a cross-sectional study design, we examined data from 2294 prediabetes and 1037 diabetics who participated in the National Health and Nutrition Examination Survey, which was conducted in the United States of America between 2007 and 2018. The dataset was randomly divided into training and validation cohorts at a ratio of 0.75-0.25. The Boruta feature selection method was used in the training cohort to identify optimal predictors for CVD diagnosis. A web-based dynamic nomogram was developed using the selected features, which were validated in the validation cohort. The Hosmer-Lemeshow test was performed to assess the nomogram's stability and performance. Receiver operating characteristics and calibration curves were used to assess the effectiveness of the nomogram. The clinical applicability of the nomogram was evaluated using decision curve analysis and clinical impact curves. In the prediabetes cohort, the CVD risk prediction nomogram included nine risk factors: age, smoking status, platelet/lymphocyte ratio, platelet count, white blood cell count, red cell distribution width, lactate dehydrogenase level, sleep disorder, and hypertension. In the diabetes cohort, the CVD risk prediction nomogram included eleven risk factors: age, material status, smoking status, systemic inflammatory response index, neutrophil-to-lymphocyte ratio, red cell distribution width, lactate dehydrogenase, high-density lipoprotein cholesterol, sleep disorder, hypertension, and physical activity. The nomogram models developed in this study have good predictive and discriminant utility for predicting CVD risk in patients with prediabetes and diabetes.

Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations.

The app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226).

On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively.

The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Multiple clinician adjustable parameters impact upon glycemia in people with type 1 diabetes (T1D) using Medtronic Mini Med 780G (MM780G) AHCL. These include glucose targets, carbohydrate ratios (CR), and active insulin time (AIT). Algorithm-based decision support advising upon potential settings adjustments may enhance clinical decision-making.

Single-arm, two-phase exploratory study developing decision support to commence and sustain AHCL. Participants commenced investigational MM780G, then 8 weeks Phase 1-initial optimization tool evaluation, involving algorithm-based decision support with weekly AIT and CR recommendations. Clinicians approved or rejected CR and AIT recommendations based on perceived safety per protocol. Co-design resulted in a refined algorithm evaluated in a further identically configured Phase 2. Phase 2 participants also transitioned to commercial MM780G following "Quick Start" (algorithm-derived tool determining initial AHCL settings using daily insulin dose and weight). We assessed efficacy, safety, and acceptability of decision support using glycemic metrics, and the proportion of accepted CR and AIT settings per phase.

Fifty three participants commenced Phase 1 (mean age 24.4; Hba1c 61.5mmol/7.7%). The proportion of CR and AIT accepted by clinicians increased between Phases 1 and 2 respectively: CR 89.2% vs. 98.6%, p < 0.01; AIT 95.2% vs. 99.3%, p < 0.01. Between Phases, mean glucose percentage time < 3.9mmol (< 70mg/dl) reduced (2.1% vs. 1.4%, p = 0.04); change in mean TIR 3.9-10mmol/L (70-180mg/dl) was not statistically significant: 72.9% ± 7.8 and 73.5% ± 8.6. Quick start resulted in stable TIR, and glycemic metrics compared to international guidelines.

The co-designed decision support tools were able to deliver safe and effective therapy. They can potentially reduce the burden of diabetes management related decision making for both health care practitioners and patients.

Prospectively registered with Australia/New Zealand Clinical Trials Registry(ANZCTR) on 30th March 2021 as study ACTRN12621000360819.

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.

Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.

One of the major problems related to type 1 diabetes (T1D) management is hypoglycemia, a condition characterized by low blood glucose levels and responsible for reduced quality of life and increased mortality. Fast-acting carbohydrates, also known as hypoglycemic treatments (HT), can counteract this event. In the literature, dosage and timing of HT are usually based on heuristic rules. In the present work, we propose an algorithm for mitigating hypoglycemia by suggesting preventive HT consumption, with dosages and timing determined by solving an optimization problem.

By leveraging integer programming and linear inequality constraints, the algorithm can bind the amount of suggested carbohydrates to standardized quantities (i.e., those available in "off-the-shelf" HT) and the minimal distance between consecutive suggestions (to reduce the nuisance for patients).

The proposed method was tested in silico and compared with competitor algorithms using the UVa/Padova T1D simulator. At the cost of a slight increase of HT consumed per day, the proposed algorithm produces the lowest median and interquartile range of the time spent in hypoglycemia, with a statistically significant improvement over most competitor algorithms. Also, the average number of hypoglycemic events per day is reduced to 0 in median.

Thanks to its positive performances and reduced computational burden, the proposed algorithm could be a candidate tool for integration in a DSS aimed at improving T1D management.

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.

Proper insulin management is vital for maintaining stable blood sugar levels and preventing complications associated with diabetes. However, the soaring costs of insulin present significant challenges to ensuring affordable management. This paper conducts a comprehensive review of current literature on the application of machine learning (ML) in insulin management for diabetes patients, particularly focusing on enhancing affordability and accessibility within the United States. The review encompasses various facets of insulin management, including dosage calculation and response, prediction of blood glucose and insulin sensitivity, initial insulin estimation, resistance prediction, treatment adherence, complications, hypoglycemia prediction, and lifestyle modifications. Additionally, the study identifies key limitations in the utilization of ML within the insulin management literature and suggests future research directions aimed at furthering accessible and affordable insulin treatments. These proposed directions include exploring insurance coverage, optimizing insulin type selection, assessing the impact of biosimilar insulin and market competition, considering mental health factors, evaluating insulin delivery options, addressing cost-related issues affecting insulin usage and adherence, and selecting appropriate patient cost-sharing programs. By examining the potential of ML in addressing insulin management affordability and accessibility, this work aims to envision improved and cost-effective insulin management practices. It not only highlights existing research gaps but also offers insights into future directions, guiding the development of innovative solutions that have the potential to revolutionize insulin management and benefit patients reliant on this life-saving treatment.

Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.

Managing diabetes is a chronic challenge today, requiring monitoring and timely insulin injections to maintain stable blood glucose levels. Traditional clinical testing relies on fingertip or venous blood collection, which has facilitated the emergence of continuous glucose monitoring (CGM) technology to address data limitations. Continuous glucose monitoring technology is recognized for tracking long-term blood glucose fluctuations, and its development, particularly in wearable devices, has given rise to compact and portable continuous glucose monitoring devices, which facilitates the measurement of blood glucose and adjustment of medication. This review introduces the development of wearable CGM-based technologies, including noninvasive methods using body fluids and invasive methods using implantable electrodes. The advantages and disadvantages of these approaches are discussed as well as the use of microneedle arrays in minimally invasive CGM. Microneedle arrays allow for painless transdermal puncture and are expected to facilitate the development of wearable CGM devices. Finally, we discuss the challenges and opportunities and look forward to the biomedical applications and future directions of wearable CGM-based technologies in biological research.

Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI.

This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals.

We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles.

Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care.

GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

Digital health applications (DiGA) supporting the management of diabetes are among the most commonly available digital health technologies. However, transparent quality assurance of DiGA and clinical proof of a positive healthcare effect is often missing, which creates skepticism of some stakeholders regarding the usage and reimbursement of these applications.

This article reviews the recently established fast-track integration of DiGA in the German reimbursement market, with emphasis on the current impact for manufacturers, healthcare providers, and people with diabetes. The German DiGA fast track is contextualised with corresponding initiatives in Europe.

The option of a provisional prescription and reimbursement of DiGA while proving a positive healthcare effect in parallel may expedite the adoption of DiGA in Germany and beyond. However, hurdles for a permanent prescription and reimbursement of DiGA are high and only one of 12 that have achieved this status specifically addresses people with diabetes.

The DiGA fast track needs to be further enhanced to cope with remaining skepticism and contribute even more to a value-based diabetes care.

Managing glucose levels during exercise is challenging for individuals with type 1 diabetes (T1D) since multiple factors including activity type, duration, intensity and other factors must be considered. Current decision support tools lack personalized recommendations and fail to distinguish between aerobic and resistance exercise. We propose an exercise-aware decision support system (exDSS) that uses digital twins to deliver personalized recommendations to help people with T1D maintain safe glucose levels (70-180 mg/dL) and avoid low glucose (<70 mg/dL) during and after exercise.

We evaluated exDSS using various exercise and meal scenarios recorded from a large, free-living study of aerobic and resistance exercise. The model inputs were heart rate, insulin, and meal data. Glucose responses were simulated during and after 30-minute exercise sessions (676 aerobic, 631 resistance) from 247 participants. Glucose outcomes were compared when participants followed exDSS recommendations, clinical guidelines, or did not modify behavior (no intervention).

exDSS significantly improved mean time in range for aerobic (80.2% to 92.3%, P < .0001) and resistance (72.3% to 87.3%, P < .0001) exercises compared with no intervention, and versus clinical guidelines (aerobic: 82.2%, P < .0001; resistance: 80.3%, P < .0001). exDSS reduced time spent in low glucose for both exercise types compared with no intervention (aerobic: 15.1% to 5.1%, P < .0001; resistance: 18.2% to 6.6%, P < .0001) and was comparable with following clinical guidelines (aerobic: 4.5%, resistance: 8.1%, P = N.S.).

The exDSS tool significantly improved glucose outcomes during and after exercise versus following clinical guidelines and no intervention providing motivation for clinical evaluation of the exDSS system.

To evaluate glucose control non-inferiority and time benefits of telemedicine follow-up in children with type 1 diabetes (CwD).

In a single-center 9-month-long randomized controlled study (clinicaltrials.gov NCT05484427), 50 children were randomized to either telemedicine group (TG) followed-up distantly by e-mail, or to face-to-face group (FFG) attending standard personal visits. The primary endpoint was non-inferiority of HbA1c at final visit (level of non-inferiority was set at 5 mmol/mol). The secondary endpoints were subcutaneous glucose monitoring parameters and time consumption from both study subjects' and the physicians' point of view.

Non-inferiority of HbA1c in the TG was proven (mean HbA1C 45.8 ± 7.3 [TG] vs. 50.0 ± 12.6 [FFG] mmol/mol, 6.3 vs. 6.7 % DCCT, p = 0.17; between groups HbA1C difference 95 % CI -10.2 to 1.9 mmol/mol). Telemedicine saved time for participants (mean visit duration [MVD] 50 [TG] vs. 247 min [FFG], p < 0.001). There were no other differences between groups neither in CGM parameters nor physician's time consumption (MVD 19 [TG] vs. 20 min [FFG], p = 0.58).

Nine-month telemedicine follow-up of the children with well-controlled T1D is not inferior to standard face-to-face visits. Telemedicine visits saved time for the participants but not for their diabetologists.

This review covers currently available cardiac implantable electronic devices (CIEDs) as well as updated progress in real-time monitoring techniques for CIEDs. A variety of implantable and wearable devices that can diagnose and monitor patients with cardiovascular diseases are summarized, and various working mechanisms and principles of monitoring techniques for Telehealth and mHealth are discussed. In addition, future research directions are presented based on the rapidly evolving research landscape including Artificial Intelligence (AI).

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

Diabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine.

We aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies.

We searched the PubMed and Web of Science databases from 2016 to 2020. Studies were included if the use of ML was reported in children with DM aged 2 to 18 years, including studies on complications, screening studies, and in silico samples. In studies following the ML workflow of training, validation, and testing of results, reporting quality was assessed via MI-CLAIM by consensus judgments of independent reviewer pairs. Positive answers to the 17 binary items regarding sufficient reporting were qualitatively summarized and counted as a proxy measure of reporting quality. The synthesis of results included testing the association of reporting quality with publication and data type, participants (human or in silico), research goals, level of code sharing, and the scientific field of publication (medical or engineering), as well as with expert judgments of clinical impact and reproducibility.

After screening 1043 records, 28 studies were included. The sample size of the training cohort ranged from 5 to 561. Six studies featured only in silico patients. The reporting quality was low, with great variation among the 21 studies assessed using MI-CLAIM. The number of items with sufficient reporting ranged from 4 to 12 (mean 7.43, SD 2.62). The items on research questions and data characterization were reported adequately most often, whereas items on patient characteristics and model examination were reported adequately least often. The representativeness of the training and test cohorts to real-world settings and the adequacy of model performance evaluation were the most difficult to judge. Reporting quality improved over time (r=0.50; P=.02); it was higher than average in prognostic biomarker and risk factor studies (P=.04) and lower in noninvasive hypoglycemia detection studies (P=.006), higher in studies published in medical versus engineering journals (P=.004), and higher in studies sharing any code of the ML pipeline versus not sharing (P=.003). The association between expert judgments and MI-CLAIM ratings was not significant.

The reporting quality of ML studies in the pediatric population with DM was generally low. Important details for clinicians, such as patient characteristics; comparison with the state-of-the-art solution; and model examination for valid, unbiased, and robust results, were often the weak points of reporting. To assess their clinical utility, the reporting standards of ML studies must evolve, and algorithms for this challenging population must become more transparent and replicable.

Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically.

Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid.

Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources.

These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

Opioid use disorder is a cause for concern globally. This study aimed to optimize methadone dose adjustments using mixed modeling and machine learning.

This retrospective study was conducted at Taichung Veterans General Hospital between January 1, 2019, and December 31, 2020. Overall, 40,530 daily dosing records and 1,508 urine opiate test results were collected from 96 patients with opioid use disorder. A two-stage approach was used to create a model of the optimized methadone dose. In Stage 1, mixed modeling was performed to analyze the association between methadone dose, age, sex, treatment duration, HIV positivity, referral source, urine opiate level, last methadone dose taken, treatment adherence, and likelihood of treatment discontinuation. In Stage 2, machine learning was performed to build a model for optimized methadone dose.

Likelihood of discontinuation was associated with reduced methadone doses (β = 0.002, 95% CI = 0.000-0.081). Correlation analysis between the methadone dose determined by physicians and the optimized methadone dose showed a mean correlation coefficient of 0.995 ± 0.003, indicating that the difference between the methadone dose determined by physicians and that determined by the model was within the allowable range (p < 0.001).

We developed a model for methadone dose adjustment in patients with opioid use disorders. By integrating urine opiate levels, treatment adherence, and likelihood of treatment discontinuation, the model could suggest automatic adjustment of the methadone dose, particularly when face-to-face encounters are impractical.

Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking.

This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care.

We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes.

Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia.

CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources.

RR2-10.37766/inplasy2022.9.0061.

Type 2 diabetes (T2DM) is highly prevalent in China, affecting over 114 million people. While mHealth interventions have shown promise, there is limited research on T2DM management apps in real-world app stores.

This study aimed to systematically search and analyze T2DM care mobile apps in the Chinese market, describing their features, and functions, and evaluating the quality of the most popular apps using validated tools.

We conducted a comprehensive search in Chinese Android and iOS app stores for T2DM management apps. We downloaded 138 eligible ones for a general review of their key features and function. We also assessed the quality of the top 20 apps from both platforms using the Mobile App Rating Scale (MARS) by both researcher and patient.

A total of 3524 apps were searched. 138 eligible apps were downloaded for general review and 29 popular apps were included for quality assessment. Most apps were designed for patient users (87.0 %) and developed by commercial companies (85.5 %). Common functions included blood glucose monitoring, diabetes education, integration with measuring devices, medication adherence reminders, teleconsultation services, and diabetes risk factor tracking. The researcher's evaluation yielded an average MARS score of 4.0 out of 5 for popular apps, with subscale scores of functionalities (4.5), aesthetics (4.1), engagement (3.7), and information (3.6). However, patient ratings were lower in functionality (3.5), aesthetics (3.4), and engagement (2.6), and the patient faced difficulties with information-related items. Similar trends were observed in subject quality items.

App developers should engage caregivers, and family members as target users, and involve government agencies as partners to improve T2DM management apps. Future apps should incorporate scientifically proven advanced functions to enhance their effectiveness. The quality assessment highlighted weaknesses in engagement and information and the importance of user-centric approaches in app development.

The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline ClinAIOps ('clinical artificial-intelligence operations'), a framework that integrates continuous therapeutic monitoring and the development of artificial intelligence (AI) for clinical care. ClinAIOps leverages three feedback loops to enable the patient to make treatment adjustments using AI outputs, the clinician to oversee patient progress with AI assistance, and the AI developer to receive continuous feedback from both the patient and the clinician. We lay out the central challenges and opportunities in the deployment of ClinAIOps by means of examples of its application in the management of blood pressure, diabetes and Parkinson's disease. By enabling more frequent and accurate measurements of a patient's health and more timely adjustments to their treatment, ClinAIOps may substantially improve patient outcomes.

Advances in the development of innovative medical devices and telehealth technologies create the potential to improve the quality and efficiency of diabetes care through collecting, aggregating, and interpreting relevant health data in ways that facilitate more informed decisions among all stakeholder groups. Although many medical societies publish guidelines for utilizing these technologies in clinical practice, we believe that the methodologies used for the selection and grading of the evidence should be revised. In this article, we discuss the strengths and limitations of the various types of research commonly used for evidence selection and grading and present recommendations for modifying the process to more effectively address the rapid pace of device and technology innovation and new product development.