Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy

AI Applications for Chronic Condition Self-Management: Scoping Review

BACKGROUND

Artificial intelligence (AI) has potential in promoting and supporting self-management in patients with chronic conditions. However, the development and application of current AI technologies to meet patients' needs and improve their performance in chronic condition self-management tasks remain poorly understood. It is crucial to gather comprehensive information to guide the development and selection of effective AI solutions tailored for self-management in patients with chronic conditions.

OBJECTIVE

This scoping review aimed to provide a comprehensive overview of AI applications for chronic condition self-management based on 3 essential self-management tasks, medical, behavioral, and emotional self-management, and to identify the current developmental stages and knowledge gaps of AI applications for chronic condition self-management.

METHODS

A literature review was conducted for studies published in English between January 2011 and October 2024. In total, 4 databases, including PubMed, Web of Science, CINAHL, and PsycINFO, were searched using combined terms related to self-management and AI. The inclusion criteria included studies focused on the adult population with any type of chronic condition and AI technologies supporting self-management. This review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.

RESULTS

Of the 1873 articles retrieved from the search, 66 (3.5%) were eligible and included in this review. The most studied chronic condition was diabetes (20/66, 30%). Regarding self-management tasks, most studies aimed to support medical (45/66, 68%) or behavioral self-management (27/66, 41%), and fewer studies focused on emotional self-management (14/66, 21%). Conversational AI (21/66, 32%) and multiple machine learning algorithms (16/66, 24%) were the most used AI technologies. However, most AI technologies remained in the algorithm development (25/66, 38%) or early feasibility testing stages (25/66, 38%).

CONCLUSIONS

A variety of AI technologies have been developed and applied in chronic condition self-management, primarily for medication, symptoms, and lifestyle self-management. Fewer AI technologies were developed for emotional self-management tasks, and most AIs remained in the early developmental stages. More research is needed to generate evidence for integrating AI into chronic condition self-management to obtain optimal health outcomes.

Electronic Monitoring of Medication Adherence to Direct Oral Anticoagulants: A Systematic Review

Strict medication adherence, which reflects the process by which patients take their medication as prescribed, is crucial for the use of direct oral anticoagulants (DOACs). Therefore, technological devices may serve as promising tools for assessing adherence. We aimed to systematically review the literature focusing on electronically monitored adherence (EMA) to DOACs. All studies indexed in EMBASE, Cochrane Library, MEDLINE, Scopus, and Web of Science from inception until September 1, 2023, were searched. Original studies targeting the query topics were included, findings were categorized and narratively synthetized. Adherence data, including the quality of data reporting bias, were evaluated using the EMERGE guideline. The review protocol was registered in the PROSPERO database (ID CRD42023441161). Out of the 5911 potential hits, 19 articles, comprising 15 research studies, were identified. These studies enrolled 4163 patients (median 72.1 years; 57.9% males), usually chronically treated with DOACs for atrial fibrillation. EMA was measured in 3451 patients by seven different devices from eight manufacturers; the median population tracked with electronic monitoring was 56 patients over 5 months per study. Observational studies resulted in 88.6% and interventional studies resulted in 92.5% of EMA to DOACs, mostly monitoring regimen and taking adherence. Two studies reported high-quality adherence data, whereas 11 reported low-quality adherence data. The item described in the EMERGE guideline as affecting adherence by measurement method, as appropriate, has rarely been addressed. This review broadens the understanding of the overall high EMA to DOACs reported across various study populations and designs. Furthermore, due to the identified gaps in current literature, it highlights the pressing need for standardized methodologies and improved adherence reporting. This study was supported by the GAUK 328322 and SVV 220665.

Patient reported tools for assessing potential medicine-related symptoms: A systematic review

BACKGROUND

Medicine-related symptom assessment tools have been developed to assist healthcare professionals in detecting potential medicine-related symptoms. This systematic review aimed to identify and evaluate the measurement properties of medicine-related symptom assessment tools.

METHOD

A systematic search was conducted in Ovid Medline, Ovid Embase, Ovid PsychInfo, and SCOPUS databases up to March 16, 2024. The primary studies that described either the development or measurement properties of a tool for identifying medicine-related symptoms were included. Screening and data extraction was done independently by two reviewers using Covidence. The methodological risk of bias and assessment results of reported measurement properties were evaluated using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist.

RESULT

Eleven studies met the inclusion criteria, reporting on nine unique tools. All included tools had sufficient content validity assessment results. The PHArmacotherapeutical Symptom Evaluation-20 (PHASE-20) had adequate to very good methodological quality internal consistency, construct validity, and reliability. The Patient-Reported Adverse Drug Event Questionnaire also showed adequate methodological quality with sufficient reliability, criterion validity, and construct validity but required over 30 min to complete. The PHASE-proxy exhibited adequate to very good methodological quality, with sufficient results in criterion validity, structural validity, internal consistency, and reliability. The Patient-Reported Outcome Measure Inquiry into Side-Effects showed sufficient content validity but lacked data on other measurement properties.

CONCLUSION

The majority of the identified tools were tested for one or more measurement properties. Among these tools, PHASE-20 is suitable for assessing medicine-related symptoms in elderly individuals who can participate independently, while PHASE-Proxy is for older adults with dementia or communication disabilities in nursing homes.

Anxiety about artificial intelligence from patient and doctor-physician

OBJECTIVE

This paper investigates the anxiety surrounding the integration of artificial intelligence (AI) in doctor-patient interactions, analyzing the perspectives of both patients and healthcare providers to identify key concerns and potential solutions.

METHODS

The study employs a comprehensive literature review, examining existing research on AI in healthcare, and synthesizes findings from various surveys and studies that explore the attitudes of patients and doctors towards AI applications in medical settings.

RESULTS

The analysis reveals that patient anxiety encompasses algorithm aversion, robophobia, lack of humanistic care, challenges in human-machine interaction, and concerns about AI's universal applicability. Doctors' anxieties stem from fears of replacement, legal liabilities, emotional impacts of work environment changes, and technological apprehension. The paper highlights the need for patient participation, humanistic care, improved interaction methods, educational training, and policy guidelines to foster public understanding and trust in AI.

CONCLUSION

The paper concludes that addressing AI anxiety in doctor-patient relationships is crucial for successfully integrating AI in healthcare. It emphasizes the importance of respecting patient autonomy, addressing the lack of humanistic care, and improving patient-AI interaction to enhance the patient experience and reduce medical errors.

PRACTICE IMPLICATIONS

The study suggests that future research should focus on understanding the needs and concerns of patients and doctors, strengthening medical humanities education, and establishing policies to guide the ethical use of AI in medicine. It also recommends public education to enhance understanding and trust in AI to improve medical services and ensure professional development and stable work environment for doctors.

Empowering nurses - a practical guide to artificial intelligence tools in healthcare settings: discussion paper

BACKGROUND

The rapid growth of artificial intelligence in healthcare is transforming how nurses deliver care and make clinical decisions. From supporting diagnostics to providing virtual health assistants, artificial intelligence offers new ways to enhance patient outcomes and streamline healthcare processes. However, these advancements also bring challenges, particularly around ethics, potential biases, and ensuring technology complements rather than replaces human expertise.

METHODS

A discussion paper designed to break down key artificial intelligence terms and demonstrate real-world applications to guide nurses to develop the skills needed to navigate this evolving technological landscape.

FINDINGS

This discussion emphasises the importance of maintaining the critical role of human clinical judgment, highlighting that artificial intelligence should support nurses' expertise rather than diminish it. The need for continuous education to keep nurses equipped with the knowledge to effectively integrate artificial intelligence into their practice is argued. With an inclusive approach, artificial intelligence has the potential to become a powerful tool that supports nurses in improving patient care while preserving the essential human touch in healthcare.

Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial

BACKGROUND

Ureteral stents, such as double-J stents, have become indispensable in urologic procedures but are associated with complications like hematuria and pain. While the advancement of artificial intelligence (AI) technology has led to its increasing application in the health sector, AI has not been used to provide information on potential complications and to facilitate subsequent measures in the event of such complications.

OBJECTIVE

This study aimed to assess the effectiveness of an AI-based prediction tool in providing patients with information about potential complications from ureteroscopy and ureteric stent placement and indicating the need for early additional therapy.

METHODS

Overall, 28 patients (aged 20-70 years) who underwent ureteral stent insertion for the first time without a history of psychological illness were consecutively included. A "reassurance-call" service was set up to equip patients with details about the procedure and postprocedure care, to monitor for complications and their severity. Patients were randomly allocated into 2 groups, reassurance-call by AI (group 1) and reassurance-call by humans (group 2). The primary outcome was the level of satisfaction with the reassurance-call service itself, measured using a Likert scale. Secondary outcomes included satisfaction with the AI-assisted reassurance-call service, also measured using a Likert scale, and the level of satisfaction (Likert scale and Visual Analogue Scale [VAS]) and anxiety (State-Trait Anxiety Inventory and VAS) related to managing complications for both groups.

RESULTS

Of the 28 recruited patients (14 in each group), 1 patient in group 2 dropped out. Baseline characteristics of patients showed no significant differences (all P>.05). Satisfaction with reassurance-call averaged 4.14 (SD 0.66; group 1) and 4.54 (SD 0.52; group 2), with no significant difference between AI and humans (P=.11). AI-assisted reassurance-call satisfaction averaged 3.43 (SD 0.94). Satisfaction about the management of complications using the Likert scale averaged 3.79 (SD 0.70) and 4.23 (SD 0.83), respectively, showing no significant difference (P=.14), but a significant difference was observed when using the VAS (P=.01), with 6.64 (SD 2.13) in group 1 and 8.69 (SD 1.80) in group 2. Anxiety about complications using the State-Trait Anxiety Inventory averaged 36.43 (SD 9.17) and 39.23 (SD 8.51; P=.33), while anxiety assessed with VAS averaged 4.86 (SD 2.28) and 3.46 (SD 3.38; P=.18), respectively, showing no significant differences. Multiple regression analysis was performed on all outcomes, and humans showed superior satisfaction than AI in the management of complications. Otherwise, most of the other variables showed no significant differences (P.>05).

CONCLUSIONS

This is the first study to use AI for patient reassurance regarding complications after ureteric stent placement. The study found that patients were similarly satisfied for reassurance calls conducted by AI or humans. Further research in larger populations is warranted to confirm these findings.

TRIAL REGISTRATION

Clinical Research Information System KCT0008062; https://tinyurl.com/4s8725w2.

Effectiveness of Mobile Health Intervention in Medication Adherence: a Systematic Review and Meta-Analysis

Low medication adherence poses a great risk of poor treatment outcomes among patients with chronic diseases. Recently, mobile applications (apps) have been recognized as effective interventions, enabling patients to adhere to their prescriptions. This study aimed to establish the effectiveness of mobile app interventions for medication adherence, affecting features, and dropout rates by focusing on previous randomized controlled trials (RCTs). This study conducted a systematic review and meta-analysis of mobile app interventions targeting medication adherence in patients with chronic diseases. Electronic searches of eight databases were conducted on April 21, 2023, for studies published between 2013 and 2023. Comprehensive meta-analysis software was used to estimate the standardized mean difference (SMD) of pooled outcomes, odds ratios (ORs), and confidence intervals (CIs). Subgroup analysis was applied to investigate and compare the effectiveness of the interventional strategies and their features. The risk of bias of the included RCTs was evaluated by applying the risk of bias tool. Publication bias was examined using the fail-safe N method. Twenty-six studies with 5,174 participants were included (experimental group 2603, control group 2571). The meta-analysis findings showed a positive impact of mobile apps on improving medication adherence (OR = 2.371, SMD = 0.279). The subgroup analysis results revealed greater effectiveness of interventions using interactive strategies (OR = 2.652, SMD = 0.283), advanced reminders (OR = 1.849, SMD = 0.455), data-sharing (OR = 2.404, SMD = 0.346), and pill dispensers (OR = 2.453). The current study found that mobile interventions had significant effects on improving medication adherence. Subgroup analysis showed that the roles of stakeholders in health providers' interactions with patients and developers' understanding of patients and disease characteristics are critical. Future studies should incorporate advanced technology reflecting acceptability and the needs of the target population.

Satisfaction and Usability of a Commercially Available Medication Adherence App (Medisafe) Among Medically Underserved Patients With Chronic Illnesses: Survey Study

BACKGROUND

Research supports the use of mobile phone apps to promote medication adherence, but the use of and satisfaction with these apps among medically underserved patients with chronic illnesses remain unclear.

OBJECTIVE

This study reports on the overall use of and satisfaction with a medication adherence app (Medisafe) in a medically underserved population.

METHODS

Medically underserved adults who received care for one or more chronic illnesses at a federally qualified health center (FQHC) were randomized to an intervention group in a larger randomized controlled trial and used the app for 1 month (n=30), after which they completed a web-based survey. Objective data on app usage were provided as secondary data by the app company.

RESULTS

The participants were very satisfied with the app, with all participants (30/30, 100%) somewhat or strongly agreeing that they would recommend the app to family and friends. Participants strongly agreed (28/30, 93%) that the reminders helped them remember to take their medications at the correct time each day, and they (28/30, 93%) found the app easy to use. Additional features accessed by some included educational features and the adherence report. Participants noted the helpfulness of having a medication list on their phones, and some used it during medication reconciliation at doctor visits. Use of the Medfriend feature, which alerts a social support person if a medication is missed, was low (n=2), but those who used it were very positive about the feature.

CONCLUSIONS

A commercially available medication adherence app was found to be useful by participants, and they were satisfied with the app and the additional features provided. The use of medication adherence mobile phone apps has the potential to positively influence chronic disease management in a medically underserved population on a large scale.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05098743; https://clinicaltrials.gov/study/NCT05098743.

Use of Technology to Improve Adherence in Allergy/Immunology

The integration of technology into health care has shown significant promise in enhancing patient adherence, particularly in the field of allergy/immunology. This article explores the multifaceted approaches through which digital health interventions can be used to improve adherence rates among patients with allergic diseases and immunologic disorders. By reviewing recent advancements in telemedicine, mobile health applications, wearable devices, and digital reminders, as well as smart inhalers, we aim to provide a comprehensive overview of how these technologies can support patients in managing their conditions. The analysis highlights the role of personalized digital health plans, which, through the use of artificial intelligence and machine learning algorithms, can offer tailored advice, monitor symptoms, and adjust treatment protocols in real time. Moreover, the article discusses the impact of electronic health records and patient portals in fostering a collaborative patient-provider relationship, thereby enhancing communication and adherence. The integration of these technologies has been shown to not only improve clinical outcomes but also increase patient satisfaction and engagement.

Effects of a novel GABA-B positive allosteric modulator, ASP8062, on alcohol cue-elicited craving and naturalistic alcohol consumption in a multisite randomized, double-blind, placebo-controlled trial

BACKGROUND

The γ-aminobutyric acid-B (GABAB) receptor is a promising target for the development of new medications to treat alcohol use disorder (AUD). The GABAB agonist baclofen has been reported to reduce alcohol consumption but is associated with some undesirable side effects, including sedation. ASP8062 is a novel compound that acts as a positive allosteric modulator at the GABAB receptor and may be more tolerable than baclofen. This proof-of-concept human laboratory clinical trial evaluated the safety profile of ASP8062 and tested its effects on cue-elicited alcohol craving and alcohol use among treatment-seeking individuals with AUD.

METHODS

This double-blind, randomized, multisite trial tested the effect of ASP8062 (25 mg once daily), relative to placebo, on alcohol cue-elicited craving in a laboratory setting and alcohol consumption, craving, mood, sleep, cigarette smoking, and alcohol-related consequences in the natural environment over a 6-week treatment period. Participants were 60 individuals (26 females and 34 males) with moderate or severe AUD.

RESULTS

ASP8062, relative to placebo, was well tolerated, and there were no adverse events (AEs) that significantly differed between treatment groups. Most AEs were mild/moderate, and there were no serious AEs among individuals treated with ASP8062. However, ASP8062 did not attenuate alcohol cue-elicited craving compared with placebo. Moreover, exploratory analyses indicated that ASP8062, relative to placebo, did not significantly affect alcohol consumption, naturalistic alcohol craving, mood, sleep, cigarette smoking, or alcohol-related negative consequences during the 6-week treatment period.

CONCLUSIONS

Although ASP8062 was well tolerated with no serious AEs, the novel compound did not significantly dampen alcohol cue-elicited craving or improve other AUD-related outcome measures. These data indicate positive allosteric modulation of the GABAB receptor at the dose evaluated here may not blunt alcohol cue-elicited craving, and preliminary drinking outcome data suggest it may not be an efficacious treatment strategy for AUD.

Coping with Complications that Occur after Prostate Biopsy for Satisfactory Evaluation of Call Service Using Artificial Intelligence: A Pilot Randomized Controlled Trial

PURPOSE

To assess whether an artificial intelligence (AI)-based reassurance-call can inform patients about potential complications and provides reassurance following a prostate biopsy.

MATERIALS AND METHODS

From October 2022 to December 2023, 42 patients aged 40 to 70 years undergoing their first prostate biopsy were recruited. The 'Reassurance-call' service was utilized to inform and monitor patients for complications. Participants were randomized into three groups: AI-assisted Reassurance-call service (Group 1), human-assisted Reassurance-call service (Group 2), and no call (Group 3). The primary outcome measured was patient satisfaction with the Reassurance-call service, assessed using a Likert scale. Secondary outcomes included satisfaction with complication management and anxiety levels, evaluated using the Likert scale, visual analog scale (VAS), and the state-trait anxiety inventory (STAI).

RESULTS

Satisfaction with Reassurance-call averaged 4.20 (standard deviation [SD] 0.56) for Group 1 and 4.71 (SD 0.47) for Group 2, showing a statistically significant difference. Satisfaction regarding complication management using Likert scale was 4.13 (SD 0.52) for Group 1, 4.43 (SD 0.76) for Group 2, and 3.79 (SD 0.80) for Group 3 with no statistically significant differences. Satisfaction regarding complication management using VAS averaged 8.33 (SD 1.23) for Group 1, 8.57 (SD 1.45) for Group 2, and 7.07 (SD 1.86) for Group 3, indicating significant differences. Anxiety levels using STAI averaged 40.00 (SD 10.54) for Group 1, 39.14 (SD 8.29) for Group 2, and 35.00 (SD 9.46) for Group 3, showing no significant differences. Anxiety levels using VAS averaged 5.07 (SD 2.79) for Group 1, 2.21 (SD 2.64) for Group 2, and 3.50 (SD 2.28) for Group 3, with significant differences observed.

CONCLUSIONS

AI demonstrated potential in enhancing patient reassurance and managing complications post-prostate biopsy, although human interaction proved superior in certain aspects. Further studies with larger cohorts are necessary to verify the effectiveness of AI-based tools.

Smartphone App for Improving Self-Awareness of Adherence to Edoxaban Treatment in Patients With Atrial Fibrillation (ADHERE-App Trial): Randomized Controlled Trial

BACKGROUND

Adherence to oral anticoagulant therapy is essential to prevent ischemic stroke in patients with atrial fibrillation (AF).

OBJECTIVE

This study aimed to evaluate whether smartphone app-based interventions improve medication adherence in patients with AF.

METHODS

This open-label, multicenter randomized controlled trial (ADHERE-App [Self-Awareness of Drug Adherence to Edoxaban Using an Automatic App Feedback System] study) enrolled patients with AF treated with edoxaban for stroke prevention. They were randomly assigned to app-conditioned feedback (intervention; n=248) and conventional treatment (control; n=250) groups. The intervention group received daily alerts via a smartphone app to take edoxaban and measure blood pressure and heart rate at specific times. The control group received only standard, guideline-recommended care. The primary end point was edoxaban adherence, measured by pill count at 3 or 6 months. Medication adherence and the proportion of adequate medication adherence, which was defined as ≥95% of continuous medication adherence, were evaluated.

RESULTS

Medication adherence at 3 or 6 months was not significantly different between the intervention and control groups (median 98%, IQR 95%-100% vs median 98%, IQR 91%-100% at 3 months, P=.06; median 98%, IQR 94.5%-100% vs median 97.5%, IQR 92.8%-100% at 6 months, P=.15). However, the proportion of adequate medication adherence (≥95%) was significantly higher in the intervention group at both time points (76.8% vs 64.7% at 3 months, P=.01; 73.9% vs 61% at 6 months, P=.007). Among patients aged >65 years, the intervention group showed a higher medication adherence value and a higher proportion of adequate medication adherence (≥95%) at 6 months.

CONCLUSIONS

There was no difference in edoxaban adherence between the groups. However, the proportion of adequate medication adherence was higher in the intervention group, and the benefit of the smartphone app-based intervention on medication adherence was more pronounced among older patients than among younger patients. Given the low adherence to oral anticoagulants, especially among older adults, using a smartphone app may potentially improve medication adherence.

TRIAL REGISTRATION

International Clinical Trials Registry Platform KCT0004754; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=28496&search_page=L.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2021-048777.

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

BACKGROUND

 Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

METHODS

 Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

RESULTS

 Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

CONCLUSION

 ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Application of Alfalfa App in the management of oral anticoagulation in patients with atrial fibrillation: a multicenter randomized controlled trial

BACKGROUND

In recent years, mobile medical technology has made great progress in chronic disease management, but its application in patients with atrial fibrillation (AF) still needs to be clarified.

OBJECTIVE

This study aims to determine whether the newly developed smartphone app for patients with AF (Alfalfa App) can improve anticoagulation knowledge, drug treatment compliance, and satisfaction of AF patients.

METHODS

Alfalfa App integrates the functions of patient education, remote consultation, and medication reminder through a simple user interface. From June 2020 to December 2020, patients with AF were recruited in five large tertiary hospitals in China. Patients were randomly divided into the Alfalfa App or routine nursing groups. Patients' knowledge, medication adherence, and satisfaction with anticoagulation were assessed using validated questionnaires at baseline, 1 month, and 3 months.

RESULTS

In this randomized controlled trial, 113 patients with AF were included, 57 patients were randomly assigned to the Alfalfa App group, and 56 patients were randomly assigned to the routine nursing group. Forty-eight patients in the Alfalfa App group completed a three-month follow-up, and 48 patients in the routine nursing group completed a three-month follow-up. Basic demographic data were comparable between the two groups. The average age of AF patients was 61.65 ± 11.01 years old, and 61.5% of them were male. With time (baseline to 3 months), the knowledge scores of the Alfalfa App group (P<.001) and the routine nursing group (P = .002) were significantly improved, the compliance scores of the routine nursing group(P<.001) and Alfalfa App group(P<.001) significantly improved. Compared with the routine nursing group, patients' knowledge level and medication compliance using the Alfalfa App at 1 month and 3 months were significantly higher (all P < .05). There were significant differences in knowledge and compliance scores between the two groups with time (all P < .05). The satisfaction degree of drug treatment in the Alfalfa App group was significantly better than that in the routine nursing group (all P < .05).

CONCLUSIONS

Alfalfa App significantly improved the anticoagulation knowledge, drug treatment compliance, and satisfaction of AF patients. In oral anticoagulation management for AF patients, mobile medical technology that integrates the functions of patient education, remote consultation, and medication reminder may be helpful.

TRIAL REGISTRATION

Registration number, ChiCTR1900024455. Registered on July 12, 2019.

Venous thromboembolism in the era of machine learning and artificial intelligence in medicine

In this review, we embark on a comprehensive exploration of venous thromboembolism (VTE) in the context of medical history and its current practice within medicine. We delve into the landscape of artificial intelligence (AI), exploring its present utility and envisioning its transformative roles within VTE management, from prevention to screening and beyond. Central to our discourse is a forward-looking perspective on the integration of AI within VTE in medicine, advocating for rigorous study design, robust validation processes, and meticulous statistical analysis to gauge the efficacy of AI applications. We further illuminate the potential of large language models and generative AI in revolutionizing VTE care, while acknowledging their inherent limitations and proposing innovative solutions to overcome challenges related to data availability and integrity, including the strategic use of synthetic data. The critical importance of navigating ethical, legal, and privacy concerns associated with AI is underscored, alongside the imperative for comprehensive governance and policy frameworks to regulate its deployment in VTE treatment. We conclude on a note of cautious optimism, where we highlight the significance of proactively addressing the myriad challenges that accompany the advent of AI in healthcare. Through diligent design, stringent validation, extensive education, and prudent regulation, we can harness AI's potential to significantly enhance our understanding and management of VTE. As we stand on the cusp of a new era, our commitment to these principles will be instrumental in ensuring that the promise of AI is fully realized within the realm of VTE care.

Impact of Interventions on Medication Adherence in Patients With Coexisting Diabetes and Hypertension

BACKGROUND

The coexistence of diabetes and hypertension is prevalent due to shared risk factors. Pharmacological treatment has been reported to be effective in managing both conditions. However, treatment effectiveness depends on the extent to which a patient adheres to their treatment. Poor adherence to long-term treatment for chronic diseases is a growing global problem of significant magnitude. Several interventions have been developed to help improve medication adherence in patients with coexisting diabetes and hypertension. This review aimed to determine the characteristics of these interventions and their impact on medication adherence.

METHODS

A systematic review of the literature was conducted using the PRISMA guidelines and registered in the PROSPERO International Registry of Systematic Reviews. Studies were searched in the databases CINAHL, Embase and Medline to identify relevant articles published during 2012-2023. The search concepts included 'medication adherence', 'hypertension', 'diabetes' and 'intervention'. Studies were included if they were in English and evaluated the impact of an intervention aimed at promoting adherence to medications for both diabetes and hypertension.

RESULTS

Seven studies met the inclusion criteria, with five demonstrating a statistically significant improvement in medication adherence. Of the five studies that improved medication adherence, four were multifaceted and one was a single-component intervention. All successful interventions addressed at least two factors influencing non-adherence. Patient education was the foundation of most of the successful interventions, supported by other strategies, such as follow-ups and reminders.

CONCLUSION

Multifaceted interventions that also included patient education had a positive impact on medication adherence in patients with coexisting diabetes and hypertension. Improving adherence in patients with coexisting diabetes and hypertension requires a multipronged approach that considers the range of factors impacting medication-taking.

PATIENT OR PUBLIC CONTRIBUTION

This systematic review provides comprehensive insights into the benefits of patient-centred approaches in intervention development and strengthening. Such patient involvement ensures that medication adherence interventions are more relevant, acceptable and effective, ultimately leading to better health outcomes and more meaningful patient engagement in healthcare research.

A Mobile Application for Anticoagulation Management in Patients After Heart Valve Replacement: A Usability Study

Purpose

Individualized anticoagulation therapy is a major challenge for patients after heart valve replacement. Mobile applications assisted by Artificial intelligence (AI) have great potential to meet the individual needs of patients. The study aimed to develop an AI technology-assisted mobile application (app) for anticoagulation management, understand patients' acceptance of such applications, and determine its feasibility.

Methods

After using the mobile application for anticoagulation management for 2 weeks, patients, doctors, and nurses rated its usability using the System Usability Scale (SUS). Additionally, semi-structured interviews were conducted with some patients, doctors, and nurses to gain insights about their thoughts and suggestions regarding the procedure.

Results

The study comprised 80 participants, including 38 patients, 18 doctors, and 24 nurses. The average SUS score for patients was 82.37±5.45; for doctors, it was 84.17±5.82; and for nurses, it was 81.88±6.44. This means the patients, physicians, and nurses rated the app highly usable. Semi-structured interviews were conducted on the app's usability with 18 participants (six nurses, three physicians, and nine patients). The interview results revealed that patients found the application of anticoagulation management simple and convenient, with high expectations for a precise dosage recommendation of anticoagulant drugs. Some patients expressed concerns regarding personal information security. Both doctors and nurses believed that elderly patients needed assistance from young family members to use the app and that it could improve patients' anticoagulant self-management ability. Some nurses also mentioned that the use of the app brought great convenience for transitional care.

Conclusion

This study confirmed the feasibility of using an AI technology-assisted mobile application for anticoagulation management in patients after heart valve replacement. To further develop this application, challenges lie in continuously improving the accuracy of recommended drug doses, obtaining family support, and ensuring information security.

Medical Doctors' Perceptions of Artificial Intelligence (AI) in Healthcare

Introduction With the current exponential expansion of robotics, implants, and imaging technologies, diagnostic processes within the healthcare industry are becoming popular platforms for artificial intelligence (AI) use. Thus, an understanding of physicians' attitudes toward AI and the extent to which medical educators are ready to work with AI is necessary. This research aimed to study doctors' perceptions of AI in healthcare. Methods A web-based questionnaire organized into four sections, namely, demographics, concepts of AI, education in AI, and implementation challenges related to AI, was designed systematically based on a literature search and circulated among medical doctors from various fields. Results Study participants exhibited a lower score toward familiarity with AI. Only 52.12% (74/142) of physicians completed the survey. The greatest challenge associated with the use of AI in therapeutic settings was found to be the degree of autonomy, with a score of 3.56. Among the participants, 67.61% felt that the lack of human supervision was the most important limiting factor in the implementation of AI in clinical practice. However, the participants demonstrated a strong interest in understanding the concepts of AI in the near future. Conclusion This study revealed a low degree of familiarity with AI, highlighting the need for medical schools and hospitals to establish specialized education and training programs for physicians to improve patient outcomes.

Artificial intelligence and atrial fibrillation: A bibliometric analysis from 2013 to 2023

Background

In the study of atrial fibrillation (AF), a prevalent cardiac arrhythmia, the utilization of artificial intelligence (AI) in diagnostic and therapeutic strategies holds the potential to address existing limitations. This research employs bibliometrics to objectively investigate research hotspots, development trends, and existing issues in the application of AI within the AF field, aiming to provide targeted recommendations for relevant researchers.

Methods

Relevant publications on the application of AI in AF field were retrieved from the Web of Science Core Collection (WoSCC) database from 2013 to 2023. The bibliometric analysis was conducted by the R (4.2.2) "bibliometrix" package and VOSviewer(1.6.19).

Results

Analysis of 912 publications reveals that the field of AI in AF is currently experiencing rapid development. The United States, China, and the United Kingdom have made outstanding contributions to this field. Acharya UR is a notable contributor and pioneer in the area. The following topics have been elucidated: AI's application in managing the risk of AF complications is a hot mature topic; AI-electrocardiograph for AF diagnosis and AI-assisted catheter ablation surgery are the emerging and booming topics; smart wearables for real-time AF monitoring and AI for individualized AF medication are niche and well-developed topics.

Conclusion

This study offers comprehensive analysis of the origin, current status, and future trends of AI applications in AF, aiming to advance the development of the field.

Medical-informed machine learning: integrating prior knowledge into medical decision systems

BACKGROUND

Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML.

METHODS

The study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models.

RESULTS

The findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios.

CONCLUSIONS

By illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.

Mobile Diagnostic Clinics

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.

Assessing the Efficacy of an AI-Powered Chatbot (ChatGPT) in Providing Information on Orthopedic Surgeries: A Comparative Study With Expert Opinion

Background The use of artificial intelligence (AI) as a tool for patient care has continued to rapidly expand. The technology has proven its utility in various applications across several specialties in a variety of applications. However, its practicality in orthopedics remains widely unknown. This study seeks to determine if the open-access software Chat Generative Pre-Trained Transformer (ChatGPT) can be a reliable source of data for patients. Questions/purposes This study aims to determine: (1) Is the open-access AI software ChatGPT capable of accurately answering commonly posed patient questions? (2) Will there be a significant difference in agreement among the study experts in the answers generated by ChatGPT? Methods A standard list of questions for six different procedures across six subspecialties is posed to ChatGPT. The procedures chosen were anterior cruciate ligament (ACL) reconstruction, microdiscectomy, total hip arthroplasty (THA), rotator cuff repair, carpal tunnel release, and ankle fracture open reduction and internal fixation. The generated answers are then compared to expert opinion using a Likert scale based on the agreement of the aforementioned experts. Results On a three-point Likert scale with 1 being disagree and 3 being agree, the mean score across all subspecialties is 2.43, indicating at least partial agreement with expert opinion. There was no significant difference in the Likert scale mean across the six subspecialties surveyed (p = 0.177). Conclusions This study shows promise in using ChatGPT as an aid in answering patient questions regarding their surgical procedures. This opens doors for the use of the software by patients for understanding and increased shared decision-making with their surgeons. However, studies with larger participation groups are necessary to ensure accuracy on a larger and broader scale as well as studies involving specific application of AI within surgeon's practice.

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

Role of a National Health Service Electronic Prescriptions Database in the Detection of Prescribing and Dispensing Issues and Adherence Evaluation of Direct Oral Anticoagulants

BACKGROUND

Anticoagulation therapy plays a crucial role in the management of atrial fibrillation (AF) by significantly reducing the risk of stroke. Direct oral anticoagulants (DOAC) became preferred over warfarin due to their superior safety and efficacy profile. Assessing adherence to anticoagulation therapy is necessary in clinical practice for optimising patient outcomes and treatment efficacy, thus emphasising its significance.

METHODS

A retrospective study utilised the Latvian National Health Service reimbursement prescriptions database, covering prescriptions for AF and flutter from January 2012 to December 2022. The proportion of days covered method was selected for adherence assessment, categorising it into three groups: (1) below 80%, (2) between 80% and 90%, and (3) above 90%.

RESULTS

A total of 1,646,648 prescriptions were analysed. Dabigatran prescriptions started declining after 2020, coinciding with a decrease in warfarin prescriptions since 2018. The total adherence levels to DOAC therapy were 69.4%. Only 44.2% of users achieved an adherence level exceeding 80%. The rate of paper prescriptions decreased from 98.5% in 2017 to 1.3% in 2022. Additionally, the utilisation of international non-proprietary names reached 79.7% in 2022. Specifically, 16.7% of patients selected a single pharmacy, whereas 27.7% visited one or two pharmacies. Meanwhile, other patients obtained medicines from multiple pharmacies.

CONCLUSIONS

The total adherence level to DOAC therapy is evaluated as low and there was no significant difference in age, gender, or "switcher" status among adherence groups. Physicians' prescribing habits have changed over a decade.

Assessing the Efficacy of ChatGPT Versus Human Researchers in Identifying Relevant Studies on mHealth Interventions for Improving Medication Adherence in Patients With Ischemic Stroke When Conducting Systematic Reviews: Comparative Analysis

BACKGROUND

ChatGPT by OpenAI emerged as a potential tool for researchers, aiding in various aspects of research. One such application was the identification of relevant studies in systematic reviews. However, a comprehensive comparison of the efficacy of relevant study identification between human researchers and ChatGPT has not been conducted.

OBJECTIVE

This study aims to compare the efficacy of ChatGPT and human researchers in identifying relevant studies on medication adherence improvement using mobile health interventions in patients with ischemic stroke during systematic reviews.

METHODS

This study used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four electronic databases, including CINAHL Plus with Full Text, Web of Science, PubMed, and MEDLINE, were searched to identify articles published from inception until 2023 using search terms based on MeSH (Medical Subject Headings) terms generated by human researchers versus ChatGPT. The authors independently screened the titles, abstracts, and full text of the studies identified through separate searches conducted by human researchers and ChatGPT. The comparison encompassed several aspects, including the ability to retrieve relevant studies, accuracy, efficiency, limitations, and challenges associated with each method.

RESULTS

A total of 6 articles identified through search terms generated by human researchers were included in the final analysis, of which 4 (67%) reported improvements in medication adherence after the intervention. However, 33% (2/6) of the included studies did not clearly state whether medication adherence improved after the intervention. A total of 10 studies were included based on search terms generated by ChatGPT, of which 6 (60%) overlapped with studies identified by human researchers. Regarding the impact of mobile health interventions on medication adherence, most included studies (8/10, 80%) based on search terms generated by ChatGPT reported improvements in medication adherence after the intervention. However, 20% (2/10) of the studies did not clearly state whether medication adherence improved after the intervention. The precision in accurately identifying relevant studies was higher in human researchers (0.86) than in ChatGPT (0.77). This is consistent with the percentage of relevance, where human researchers (9.8%) demonstrated a higher percentage of relevance than ChatGPT (3%). However, when considering the time required for both humans and ChatGPT to identify relevant studies, ChatGPT substantially outperformed human researchers as it took less time to identify relevant studies.

CONCLUSIONS

Our comparative analysis highlighted the strengths and limitations of both approaches. Ultimately, the choice between human researchers and ChatGPT depends on the specific requirements and objectives of each review, but the collaborative synergy of both approaches holds the potential to advance evidence-based research and decision-making in the health care field.

Heart Failure Management through Telehealth: Expanding Care and Connecting Hearts

Heart failure (HF) is a leading cause of morbidity worldwide, imposing a significant burden on deaths, hospitalizations, and health costs. Anticipating patients' deterioration is a cornerstone of HF treatment: preventing congestion and end organ damage while titrating HF therapies is the aim of the majority of clinical trials. Anyway, real-life medicine struggles with resource optimization, often reducing the chances of providing a patient-tailored follow-up. Telehealth holds the potential to drive substantial qualitative improvement in clinical practice through the development of patient-centered care, facilitating resource optimization, leading to decreased outpatient visits, hospitalizations, and lengths of hospital stays. Different technologies are rising to offer the best possible care to many subsets of patients, facing any stage of HF, and challenging extreme scenarios such as heart transplantation and ventricular assist devices. This article aims to thoroughly examine the potential advantages and obstacles presented by both existing and emerging telehealth technologies, including artificial intelligence.

Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.

The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine

In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.

Artificial intelligence and machine learning for clinical pharmacology

Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.

Intake reminders are effective in enhancing adherence to direct oral anticoagulants in stroke patients: a randomised cross-over trial (MAAESTRO study)

BACKGROUND

Direct oral anticoagulants (DOAC) effectively prevent recurrent ischaemic events in atrial fibrillation (AF) patients with recent stroke. However, excellent adherence to DOAC is mandatory to guarantee sufficient anticoagulation as the effect quickly subsides.

AIM

To investigate the effect of intake reminders on adherence to DOAC.

METHODS

MAAESTRO was a randomised, cross-over study in DOAC-treated AF patients hospitalised for ischaemic stroke. Adherence was measured by electronic monitoring for 12 months. After an observational phase, patients were randomised to obtain an intake reminder either in the first or the second half of the subsequent 6-month interventional phase. The primary outcome was 100%-timing adherence. Secondary outcomes were 100%-taking adherence, and overall timing and taking adherence. We analysed adherence outcomes using McNemar's test or mixed-effects logistic models.

RESULTS

Between January 2018 and March 2022, 130 stroke patients were included, of whom 42 dropped out before randomisation. Analysis was performed with 84 patients (mean age: 76.5 years, 39.3% women). A 100%-timing adherence was observed in 10 patients who were using the reminder, and in zero patients without reminder (p = 0.002). The reminder significantly improved adherence to DOAC, with study participants having 2.7-fold increased odds to achieve an alternative threshold of 90%-timing adherence (OR 2.65; 95% CI 1.05-6.69; p = 0.039). A similar effect was observed for 90%-taking adherence (OR 3.06; 95% CI 1.20-7.80; p = 0.019). Overall timing and taking adherence increased significantly when using the reminder (OR 1.70; 95% CI 1.55-1.86, p < 0.01; and OR 1.67; 95% CI 1.52-1.84; p < 0.01).

CONCLUSION

Intake reminders increased adherence to DOAC in patients with stroke attributable to atrial fibrillation.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03344146.

Application of eHealth Tools in Anticoagulation Management After Cardiac Valve Replacement: Scoping Review Coupled With Bibliometric Analysis

BACKGROUND

Anticoagulation management can effectively prevent complications in patients undergoing cardiac valve replacement (CVR). The emergence of eHealth tools provides new prospects for the management of long-term anticoagulants. However, there is no comprehensive summary of the application of eHealth tools in anticoagulation management after CVR.

OBJECTIVE

Our objective is to clarify the current state, trends, benefits, and challenges of using eHealth tools in the anticoagulation management of patients after CVR and provide future directions and recommendations for development in this field.

METHODS

This scoping review follows the 5-step framework developed by Arksey and O'Malley. We searched 5 databases such as PubMed, MEDLINE, Web of Science, CINAHL, and Embase using keywords such as "eHealth," "anticoagulation," and "valve replacement." We included papers on the practical application of eHealth tools and excluded papers describing the underlying mechanisms for developing eHealth tools. The search time ranged from the database inception to March 1, 2023. The study findings were reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Additionally, VOSviewer (version 1.6.18) was used to construct visualization maps of countries, institutions, authors, and keywords to investigate the internal relations of included literature and to explore research hotspots and frontiers.

RESULTS

This study included 25 studies that fulfilled the criteria. There were 27,050 participants in total, with the sample size of the included studies ranging from 49 to 13,219. The eHealth tools mainly include computer-based support systems, electronic health records, telemedicine platforms, and mobile apps. Compared to traditional anticoagulation management, eHealth tools can improve time in therapeutic range and life satisfaction. However, there is no significant impact observed in terms of economic benefits and anticoagulation-related complications. Bibliometric analysis suggests the potential for increased collaboration and opportunities among countries and academic institutions. Italy had the widest cooperative relationships. Machine learning and artificial intelligence are the popular research directions in anticoagulation management.

CONCLUSIONS

eHealth tools exhibit promise for clinical applications in anticoagulation management after CVR, with the potential to enhance postoperative rehabilitation. Further high-quality research is needed to explore the economic benefits of eHealth tools in long-term anticoagulant therapy and the potential to reduce the occurrence of adverse events.

Current and Emerging Technologies to Address the Placebo Response Challenge in CNS Clinical Trials: Promise, Pitfalls, and Pathways Forward

Excessive placebo response rates have long been a major challenge for central nervous system (CNS) drug discovery. As CNS trials progressively shift toward digitalization, decentralization, and novel remote assessment approaches, questions are emerging about whether innovative technologies can help mitigate the placebo response. This article begins with a conceptual framework for understanding placebo response. We then critically evaluate the potential of a range of innovative technologies and associated research designs that might help mitigate the placebo response and enhance detection of treatment signals. These include technologies developed to directly address placebo response; technology-based approaches focused on recruitment, retention, and data collection with potential relevance to placebo response; and novel remote digital phenotyping technologies. Finally, we describe key scientific and regulatory considerations when evaluating and selecting innovative strategies to mitigate placebo response. While a range of technological innovations shows potential for helping to address the placebo response in CNS trials, much work remains to carefully evaluate their risks and benefits.

Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews

Introduction

The use of artificial intelligence (AI), which can emulate human intelligence and enhance clinical results, has grown in healthcare decision-making due to the digitalization effects and the COVID-19 pandemic. The purpose of this study was to determine the scope of applications of AI tools in the decision-making process in healthcare service delivery networks.

Materials and methods

This study used a qualitative method to conduct a systematic review of the existing reviews. Review articles published between 2000 and 2024 in English-language were searched in PubMed, Scopus, ProQuest, and Cochrane databases. The CASP (Critical Appraisal Skills Programme) Checklist for Systematic Reviews was used to evaluate the quality of the articles. Based on the eligibility criteria, the final articles were selected and the data extraction was done independently by 2 authors. Finally, the thematic analysis approach was used to analyze the data extracted from the selected articles.

Results

Of the 14 219 identified records, 18 review articles were eligible and included in the analysis, which covered the findings of 669 other articles. The quality assessment score of all reviewed articles was high. And, the thematic analysis of the data identified 3 main themes including clinical decision-making, organizational decision-making, and shared decision-making; which originated from 8 subthemes.

Conclusions

This study revealed that AI tools have been applied in various aspects of healthcare decision-making. The use of AI can improve the quality, efficiency, and effectiveness of healthcare services by providing accurate, timely, and personalized information to support decision-making. Further research is needed to explore the best practices and standards for implementing AI in healthcare decision-making.

Artificial Intelligence Readiness Among Jordanian Medical Students: Using Medical Artificial Intelligence Readiness Scale For Medical Students (MAIRS-MS)

BACKGROUND

Artificial intelligence (AI) application is increasingly used in all fields, especially, in medicine. However, for the successful incorporation of AI-driven tools into medicine, healthcare professional should be equipped with the necessary knowledge. From that, we aimed to assess the AI readiness among medical students in Jordan.

METHODS

A cross-sectional survey was conducted among medical students across 6 Jordanian universities. Prevalidated Medical Artificial Intelligence Readiness Scale for Medical Students questionnaire was used. The questionnaire was distributed through social media groups of students. SPSS v.27 was used for analysis.

RESULTS

A total of 858 responses were collected. The mean AI readiness score was 64.2%. Students scored more in the ability domain with a mean of 22.57. We found that academic performance (Grade point average) positively associated with overall AI readiness (P = .023), and prior exposure to AI through formal education or experience significantly enhances readiness (P = .009). In contrast, AI readiness levels did not significantly vary across different medical schools in Jordan. Notably, most students (84%) did not receive a formal education about AI from their schools.

CONCLUSION

Incorporation of AI education in medical curricula is crucial to close knowledge gaps and ensure that students are prepared for the use of AI in their future career. Our findings highlight the importance of preparing students to engage with AI technologies, and to be equipped with the necessary knowledge about its aspect.

Youth Ending the HIV Epidemic (YEHE): Protocol for a pilot of an automated directly observed therapy intervention with conditional economic incentives among young adults with HIV

BACKGROUND

Young adults have a disproportionately high rate of HIV infection, high rates of attrition at all stages of the HIV care continuum, and an elevated probability of disease progression and transmission. Tracking and monitoring objective measures of antiretroviral therapy (ART) adherence in real time is critical to bolster the accuracy of research data, support adherence, and improve clinical outcomes. However, adherence monitoring often relies on self-reported and retrospective data or requires additional effort from providers to understand individual adherence patterns. In this study, we will monitor medication-taking using a real-time objective measure of adherence that does not rely on self-report or healthcare providers for measurement.

METHODS

The Youth Ending the HIV Epidemic (YEHE) study will pilot a novel automated directly observed therapy-conditional economic incentive (aDOT-CEI) intervention to improve ART adherence among youth with HIV (YWH) in California and Florida who have an unsuppressed HIV viral load. The aDOT app uses facial recognition to record adherence each day, and then economic incentives are given based on a participant's confirmed adherence. We will enroll participants in a 3-month pilot study to assess the feasibility and acceptability of the aDOT-CEI intervention using predefined metrics. During and after the trial, a subsample of the pilot participants and staff/providers from participating AIDS Healthcare Foundation (AHF) clinics will participate in individual in-depth interviews to explore intervention and implementation facilitators and barriers.

DISCUSSION

YEHE will provide data on the use of an aDOT-CEI intervention to improve adherence among YWH who are not virologically suppressed. The YEHE study will document the feasibility and acceptability and will explore preliminary data to inform a trial to test the efficacy of aDOT-CEI. This intervention has the potential to effectively improve ART adherence and virologic suppression among a key population experiencing health disparities.

TRIAL REGISTRATION

The trial registration number is NCT05789875.

Effect of a Medication Adherence Mobile Phone App on Medically Underserved Patients with Chronic Illness: Preliminary Efficacy Study

BACKGROUND

Medication adherence is vital in the treatment of patients with chronic illness who require long-term medication therapies to maintain optimal health. Medication adherence, a complex and widespread problem, has been difficult to solve. Additionally, lower-income, medically underserved communities have been found to have higher rates of inadequate adherence to oral medications. Even so, this population has been underrepresented in studies using mobile medication adherence app interventions. Federally qualified health centers provide care for medically underserved populations, defined as communities and populations where there is a demonstrable unmet need for health services. These centers have been reporting an increase in a more complex chronic disease population. Including medically underserved individuals in mobile health studies provides opportunities to support this disproportionately affected group, work toward reducing health disparities in access to health care, and understand barriers to mobile health uptake.

OBJECTIVE

The aim of this preliminary efficacy study was to evaluate the effects and feasibility of a commercially available medication adherence app, Medisafe, in a medically underserved adult population with various chronic illnesses seeking care in a federally qualified health center.

METHODS

Participants in this single-arm pre-post intervention preliminary efficacy study (N=10) completed a baseline survey, used the app for 2 weeks, and completed an end-of-study survey. The primary outcome measures were medication adherence and medication self-efficacy. Feedback on the use of the app was also gathered.

RESULTS

A statistically significant median increase of 8 points on the self-efficacy for adherence to medications scale was observed (P=.03, Cohen d=0.69). Though not significant, the adherence to refills and medications scale demonstrated a median change of 2.5 points in the direction of increased medication adherence (P=.21, Cohen d=0.41). Feedback about the app was positive.

CONCLUSIONS

Use of the Medisafe app is a viable option to improve medication self-efficacy and medication adherence in medically underserved patients in an outpatient setting with a variety of chronic illnesses.

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI]

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

Mobile Technology-Based Interventions for Stroke Self-Management Support: Scoping Review

BACKGROUND

There is growing interest in enhancing stroke self-management support using mobile health (mHealth) technology (eg, smartphones and apps). Despite this growing interest, "self-management support" is inconsistently defined and applied in the poststroke mHealth intervention literature, which limits efforts to synthesize and compare evidence. To address this gap in conceptual clarity, a scoping review was conducted.

OBJECTIVE

The objectives were to (1) identify and describe the types of poststroke mHealth interventions evaluated using a randomized controlled trial design, (2) determine whether (and how) such interventions align with well-accepted conceptualizations of self-management support (the theory by Lorig and Holman and the Practical Reviews in Self-Management Support [PRISMS] taxonomy by Pearce and colleagues), and (3) identify the mHealth functions that facilitate self-management.

METHODS

A scoping review was conducted according to the methodology by Arksey and O'Malley and Levac et al. In total, 7 databases were searched. Article screening and data extraction were performed by 2 reviewers. The data were analyzed using descriptive statistics and content analysis.

RESULTS

A total of 29 studies (26 interventions) were included. The interventions addressed 7 focal areas (physical exercise, risk factor management, linguistic exercise, activities of daily living training, medication adherence, stroke education, and weight management), 5 types of mobile devices (mobile phones or smartphones, tablets, wearable sensors, wireless monitoring devices, and laptops), and 7 mHealth functions (educating, communicating, goal setting, monitoring, providing feedback, reminding, and motivating). Collectively, the interventions aligned well with the concept of self-management support. However, on an individual basis (per intervention), the alignment was less strong.

CONCLUSIONS

On the basis of the results, it is recommended that future research on poststroke mHealth interventions be more theoretically driven, more multidisciplinary, and larger in scale.

Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention

Platelets play a critical role in blood clotting and the development of arterial blockages. Antiplatelet therapy is vital for preventing recurring events in conditions like coronary artery disease and strokes. However, there is a lack of comprehensive guidelines for using antiplatelet agents in elective neurosurgery. Continuing therapy during surgery poses a bleeding risk, while discontinuing it before surgery increases the risk of thrombosis. Discontinuation is recommended in neurosurgical settings but carries an elevated risk of ischemic events. Conversely, maintaining antithrombotic therapy may increase bleeding and the need for transfusions, leading to a poor prognosis. Artificial intelligence (AI) holds promise in making difficult decisions regarding antiplatelet therapy. This paper discusses current clinical guidelines and supported regimens for antiplatelet therapy in neurosurgery. It also explores methodologies like P2Y12 reaction units (PRU) monitoring and thromboelastography (TEG) mapping for monitoring the use of antiplatelet regimens as well as their limitations. The paper explores the potential of AI to overcome such limitations associated with PRU monitoring and TEG mapping. It highlights various studies in the field of cardiovascular and neuroendovascular surgery which use AI prediction models to forecast adverse outcomes such as ischemia and bleeding, offering assistance in decision-making for antiplatelet therapy. In addition, the use of AI to improve patient adherence to antiplatelet regimens is also considered. Overall, this research aims to provide insights into the use of antiplatelet therapy and the role of AI in optimizing treatment plans in neurosurgical settings.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Prescriptive Method for Optimizing Cost of Data Collection and Annotation in Machine Learning of Clinical Ultrasound

Machine learning in medical ultrasound faces a major challenge: the prohibitive costs of producing and annotating clinical data. Optimizing the data collection and annotation will improve model training efficiency, reducing project cost and times. This paper prescribes a 2-phase method for cost optimization based on iterative accuracy/sample size predictions, and active learning for annotation optimization.

METHODS

Using public breast, fetal, and lung ultrasound datasets we can: Optimize data collection by statistically predicting accuracy for a desired dataset size; and optimize labeling efficiency using Active Learning, where predictions with lowest certainty were labelled manually using feedback. A practical case study on BUSI data was used to demonstrate the method prescribed in this work.

RESULTS

With small data subsets, ~10%, dataset size vs. final accuracy relations can be predicted with diminishing results after 50% usage. Manual annotation was reduced by ~10% using active learning to focus the annotation.

CONCLUSION

This led to cost reductions of 50%-66%, depending on requirements and initial cost model, on BUSI dataset with a negligible accuracy drop of 3.75% from theoretical maximums.Clinical Relevance- This work provides methodology to optimize dataset size and manual data labelling, this allows generation of cost-effective datasets, of interest to all, but particularly for financially limited trials and feasibility studies, Reducing the time burden on annotating clinicians.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Phenotype-directed clinically driven low-dose direct oral anticoagulant for atrial fibrillation

Clinically-driven dose reduction of direct oral anticoagulants in individuals with atrial fibrillation is prevalent worldwide. However, a paucity of evidence to tailor dose selection remained as clinical unmet need. Current doses of anticoagulant were determined largely by landmark clinical trials, in which the enrolled subjects were carefully selected and without major comorbidities. Our study reviewed the relevant real-world studies in specific patient phenotypes, including renal and hepatic diseases, elderly, low body weight, Asians and presence of concomitant drug-drug interactions. Thorough investigations toward the efficacy and safety of direct oral anticoagulants in reduced doses will facilitate substituting current universal approach with individualized prescriptions.

The ReInforcement of adherence via self-monitoring app orchestrating biosignals and medication of RivaroXaban in patients with atrial fibrillation and co-morbidities: a study protocol for a randomized controlled trial (RIVOX-AF)

Background

Because of the short half-life of non-vitamin K antagonist oral anticoagulants (NOACs), consistent drug adherence is crucial to maintain the effect of anticoagulants for stroke prevention in atrial fibrillation (AF). Considering the low adherence to NOACs in practice, we developed a mobile health platform that provides an alert for drug intake, visual confirmation of drug administration, and a list of medication intake history. This study aims to evaluate whether this smartphone app-based intervention will increase drug adherence compared with usual care in patients with AF requiring NOACs in a large population.

Methods

This prospective, randomized, open-label, multicenter trial (RIVOX-AF study) will include a total of 1,042 patients (521 patients in the intervention group and 521 patients in the control group) from 13 tertiary hospitals in South Korea. Patients with AF aged ≥19 years with one or more comorbidities, including heart failure, myocardial infarction, stable angina, hypertension, or diabetes mellitus, will be included in this study. Participants will be randomly assigned to either the intervention group (MEDI-app) or the conventional treatment group in a 1:1 ratio using a web-based randomization service. The intervention group will use a smartphone app that includes an alarm for drug intake, visual confirmation of drug administration through a camera check, and presentation of a list of medication intake history. The primary endpoint is adherence to rivaroxaban by pill count measurements at 12 and 24 weeks. The key secondary endpoints are clinical composite endpoints, including systemic embolic events, stroke, major bleeding requiring transfusion or hospitalization, or death during the 24 weeks of follow-up.

Discussion

This randomized controlled trial will investigate the feasibility and efficacy of smartphone apps and mobile health platforms in improving adherence to NOACs.

Trial registration

The study design has been registered in ClinicalTrial.gov (NCT05557123).

Effectiveness of Remote Interventions to Improve Medication Adherence in Patients after Stroke: A Systematic Literature Review and Meta-Analysis

BACKGROUND

Stroke affects more than 30 million people every year, but only two-thirds of patients comply with prescribed medication, leading to high stroke recurrence rates. Digital technologies can facilitate interventions to support treatment adherence.

PURPOSE

This study evaluates the effectiveness of remote interventions and their mechanisms of action in supporting medication adherence after stroke.

METHODS

PubMed, MEDLINE via Ovid, Cochrane CENTRAL, the Web of Science, SCOPUS, and PsycINFO were searched, and meta-analysis was performed using the Review Manager Tool. Intervention content analysis was conducted based on the COM-B model.

RESULTS

Ten eligible studies were included in the review and meta-analysis. The evidence suggested that patients who received remote interventions had significantly better medication adherence (SMD 0.49, 95% CI [0.04, 0.93], and p = 0.03) compared to those who received the usual care. The adherence ratio also indicated the interventions' effectiveness (odds ratio 1.30, 95% CI [0.55, 3.10], and p = 0.55). The systolic and diastolic blood pressure (MD -3.73 and 95% CI [-5.35, -2.10])/(MD -2.16 and 95% CI [-3.09, -1.22]) and cholesterol levels (MD -0.36 and 95% CI [-0.52, -0.20]) were significantly improved in the intervention group compared to the control. Further behavioural analysis demonstrated that enhancing the capability within the COM-B model had the largest impact in supporting improvements in adherence behaviour and relevant clinical outcomes. Patients' satisfaction and the interventions' usability were both high, suggesting the interventions' acceptability.

CONCLUSION

Telemedicine and mHealth interventions are effective in improving medication adherence and clinical indicators in stroke patients. Future studies could usefully investigate the effectiveness and cost-effectiveness of theory-based and remotely delivered interventions as an adjunct to stroke rehabilitation programmers.

[Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionDiretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI]

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI]

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.