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

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.

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.

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.

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.

“What are the roles and competencies of doctors in the artificial intelligence implementation society?: A qualitative analysis through physician interview” (Preprint)

BACKGROUND

Artificial intelligence (AI) is a term used to describe the use of computers and technology to emulate human intelligence mechanisms. Although AI is known to affect health services, the impact of information provided by AI on the patient-physician relationship in actual practice is unclear.

OBJECTIVE

The purpose of this study is to investigate the effect of introducing AI functions into the medical field on the role of the physician or physician-patient relationship, as well as potential concerns in the AI era.

METHODS

We conducted focus group interviews in Tokyo's suburbs with physicians recruited through snowball sampling. The interviews were conducted in accordance with the questions listed in the interview guide. A verbatim transcript recording of all interviews was qualitatively analyzed using content analysis by all authors. Similarly, extracted code was grouped into subcategories, categories, and then core categories. We continued interviewing, analyzing, and discussing until we reached data saturation. In addition, we shared the results with all interviewees and confirmed the content to ensure the credibility of the analysis results.

RESULTS

A total of 9 participants who belonged to various clinical departments in the 3 groups were interviewed. The same interviewers conducted the interview as the moderator each time. The average group interview time for the 3 groups was 102 minutes. Content saturation and theme development were achieved with the 3 groups. We identified three core categories: (1) functions expected to be replaced by AI, (2) functions still expected of human physicians, and (3) concerns about the medical field in the AI era. We also summarized the roles of physicians and patients, as well as the changes in the clinical environment in the age of AI. Some of the current functions of the physician were primarily replaced by AI functions, while others were inherited as the functions of the physician. In addition, "functions extended by AI" obtained by processing massive amounts of data will emerge, and a new role for physicians will be created to deal with them. Accordingly, the importance of physician functions, such as responsibility and commitment based on values, will increase, which will simultaneously increase the expectations of the patients that physicians will perform these functions.

CONCLUSIONS

We presented our findings on how the medical processes of physicians and patients will change as AI technology is fully implemented. Promoting interdisciplinary discussions on how to overcome the challenges is essential, referring to the discussions being conducted in other fields.

Are physicians and medical students ready for artificial intelligence applications in healthcare?

Background

Artificial intelligence (AI) Healthcare applications are listed in the national visions of some Gulf Cooperation Council countries. A successful use of AI depends on the attitude and perception of medical experts of its applications.

Objective

To evaluate physicians and medical students' attitude and perception on AI applications in healthcare.

Method

A web-based survey was disseminated by email to physicians and medical students.

Results

A total of 293 (82 physicians and 211 medical students) individuals have participated (response rate is 27%). Seven participants (9%) reported knowing nothing about AI, while 208 (69%) were aware that it is an emerging field and would like to learn about it. Concerns about AI impact on physicians' employability were not prominent. Instead, the majority (n=159) agreed that new positions will be created and the job market for those who embrace AI will increase. They reported willingness to adapt AI in practice if it was incorporated in international guidelines (30.5%), published in respected scientific journals (17.1%), or included in formal training (12.2%). Almost two of the three participants agreed that dedicated courses will help them to implement AI. The most commonly reported problem of AI is its inability to provide opinions in unexpected scenarios. Half of the participants think that both the manufacturer and physicians should be legally liable for medical errors occur due to AI-based decision support tools while more than one-third (36.77%) think that physicians who make the final decision should be legally liable. Senior physicians were found to be less familiar with AI and more concerned about physicians' legal liability in case of a medical error.

Conclusion

Physicians and medical students showed positive attitudes and willingness to learn about AI applications in healthcare. Introducing AI learning objectives or short courses in medical curriculum would help to equip physicians with the needed skills for AI-augmented healthcare system.

Randomized Controlled Trial of Automated Directly Observed Therapy for Measurement and Support of PrEP Adherence Among Young Men Who have Sex with Men

Measurement of adherence to oral pre-exposure prophylaxis (PrEP) in real-time has been challenging. We developed DOT Diary, a smartphone application that combines automated directly observed therapy with a PrEP adherence visualization toolkit, and tested its ability to measure PrEP adherence and to increase adherence among a diverse cohort of young men who have sex with men (MSM). We enrolled 100 MSM in San Francisco and Atlanta and randomly assigned them 2:1 to DOT Diary versus standard of care. Concordance between DOT Diary measurement and drug levels in dried blood spots was substantial, with 91.0% and 85.3% concordance between DOT Diary and emtricitabine-triphosphate and tenofovir-diphosphate, respectively. There was no significant difference in the proportion of participants with detectable PrEP drug levels at 24 weeks between study arms. These results suggest DOT Diary is substantially better than self-reported measures of adherence, but additional interventions are needed to improve PrEP adherence over time.

Challenges and Possible Solutions to Direct-Acting Oral Anticoagulants (DOACs) Dosing in Patients with Extreme Bodyweight and Renal Impairment

This article aims to highlight the dosing issues of direct oral anticoagulants (DOACs) in patients with renal impairment and/or obesity in an attempt to develop solutions employing advanced data-driven techniques. DOACs have become widely accepted by clinicians worldwide because of their superior clinical profiles, more predictable pharmacokinetics, and hence more convenient dosing relative to other anticoagulants. However, the optimal dosing of DOACs in extreme bodyweight  patients and patients with renal impairment is difficult to achieve using the conventional dosing approach. The standard dosing approach (fixed-dose) is based on limited data from clinical studies. The existing formulae (models) for determining the appropriate doses for these patient groups leads to suboptimal dosing. This problem of mis-dosing is worsened by the lack of standardized laboratory parameters for monitoring the exposure to DOACs in renal failure and extreme bodyweight patients. Model-informed precision dosing (MIPD) encompasses a range of techniques like machine learning and pharmacometrics modelling, which could uncover key variables and relationships as well as shed more light on the pharmacokinetics and pharmacodynamics of DOACs in patients with extreme bodyweight or renal impairment. Ultimately, this individualized approach-if implemented in clinical practice-could optimise dosing for the DOACs for better safety and efficacy.

Construction of Stomach Cancer Lesion Detection Combined with Drug Therapy Based on Artificial Intelligence

The number of stomach cancer (SC) patients is increasing sharply every year, and gastroscope is a common method to check stomach-related diseases. A bulging lesion in the stomach is encountered during a gastroscopy. Due to the change in eating habits, the enhancement of health awareness, and the wide application of gastroscopy, the detection rate and cure rate of tumors have been significantly improved. This has certain clinical value for the early diagnosis and treatment of early SC. In this paper, based on the background of artificial intelligence, image segmentation technology is used to analyze and process the detection results of SC, so as to judge the effect of drug treatment. A total of 1408 gastric bulge lesions were investigated in 11023 patients during the one-year period 2019-2020. It also analyzed the age, lesion location, size, pathological type, and tumor detection results of 1408 patients. The experiment showed that among the 289 cases of submucosal bulging lesions, the detection rates of the young group, middle-aged group, and elderly group were 14.9% (43/289), 67.5% (195/289), and 17.6% (51/289), respectively. Among them, middle-aged people aged 41-65 have the highest detection rate. The incidence of gastric polyps was similar between different age groups. But with age, the rate of fundic gland polyps increases. The incidence of SC is not related to the age of the patient, but to its pathological type. The incidence of SC in middle-aged and elderly people is significantly higher than that in young people. SC is more common in the cardia, and gastrointestinal stromal tumors are most common with submucosal protrusion.

A smartphone application to improve adherence to vitamin and mineral supplementation after bariatric surgery

OBJECTIVE

This trial evaluated a smartphone application's effectiveness in improving adherence to vitamin and mineral supplementation postoperatively.

METHODS

This study was a randomized controlled trial comprising 140 patients undergoing bariatric surgery (gastric bypass or sleeve gastrectomy). Participants were randomized 1:1 to the 12-week intervention, using the smartphone application PromMera, or to standard care. The primary end point was adherence to vitamin and mineral supplementation.

RESULTS

Initiation rate and overall adherence to supplementation were high in both groups. Change in objectively measured adherence rate from before the intervention to 1 year post surgery, measured with pharmacy refill data, did not differ between groups for vitamin B₁₂ (-9.6% [SD = 27%] vs. -9.3% [SD = 30%]; p = 0.48) or calcium/vitamin D (-12.3% [SD = 29%] vs. -11.5% [SD = 32%]; p = 0.44). A modest effect on the secondary end point (subjectively measured adherence, using the Medication Adherence Report Scale-5) was seen immediately after the intervention (intervention group 0.00 [SD = 1.3] vs. control group -1.2 [SD = 3.5]; p = 0.021), but this effect did not persist 1 year post surgery. No differences were detected in the prevalence of biochemical deficiencies.

CONCLUSIONS

The use of the smartphone application PromMera did not obtain a lasting improvement in adherence to vitamin and mineral supplementation 1 year post bariatric surgery.

Effects of Chronic Heart Failure on Longitudinal Changes of Cognitive Function in Elderly Patients

Elderly patients with cognitive impairment present problems such as memory loss, impaired judgment, and loss of language function. Severe cases affect daily life and social functions. For the past few years, the possible disease and possible illness of chronic heart failure (CHF) in elderly patients have continued to increase. However, there is not enough research on the effect of CHF on the longitudinal change speed of cognitive function in elderly patients. Most studies focus on the effects of diseases like hypertension, coronary heart disease, and diabetes on cognitive function in elderly patients, which results in incomplete research. In this context, this article used a Bayesian network to build a cognitive function classification model for the elderly. The effects of CHF on the rate of longitudinal changes in cognitive function in elderly patients were studied by mental state examination and statistical methods. The experiment finally concluded by evaluating the attention, language ability, and memory ability of elderly patients. In the elderly with CHF, the incidence of cognitive impairment was about 71.66%. The experimental results further indicated that the higher the degree of CHF in elderly patients, the lower the level of cognitive function. This article could help advance research on preventing or delaying cognitive decline in patients with CHF.

Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background

With the development of Internet technology and the arrival of the knowledge-driven era, the breadth and depth of educational informatization are increasing day by day. Educational technology is not only a subject but also a career adapted to education and teaching. The growth speed of modern educational technology and the size of its benefits determine its management level to a large extent. With new technologies, new ideas, and new social needs, it is difficult for new ideas, new thoughts, and new methods to make the traditional e-learning management to accommodate the demands of the new era. At present, the work efficiency of modern educational technology visualization systems is generally not high, and modern distance teaching has an increasing demand for management informatization. However, there is a lack of a management platform for distance education that adapts to organizational characteristics such as openness, dynamics, flexibility, individualization, and decentralization. Therefore, this study introduces machine learning and BP neural network, establishes a visual modern distance teaching management system model, and uses machine learning algorithms to learn the visual process. The experimental results show that the system efficiency after learning is higher, and the time required for visualization of different groups in the experiment is 14.32 s, 13.18 s, 12.27 s, and 13.64 s, respectively, which effectively improves the efficiency of visualization and reduces the consumption of human resources.

mHealth Apps Using Behavior Change Techniques to Self-report Data: Systematic Review

Background

The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management.

Objective

This review aimed to identify behavior change techniques (BCTs) commonly used in mHealth, assess their effectiveness based on the evidence reported in interventions and reviews to highlight the most appropriate techniques to design an optimal strategy to improve adherence to data reporting, and provide recommendations for future interventions and research.

Methods

We performed a systematic review of studies published between 2010 and 2021 in relevant scientific databases to identify and analyze mHealth interventions using BCTs that evaluated their effectiveness in terms of user adherence. Search terms included a mix of general (eg, data, information, and adherence), computer science (eg, mHealth and BCTs), and medicine (eg, personalized medicine) terms.

Results

This systematic review included 24 studies and revealed that the most frequently used BCTs in the studies were feedback and monitoring (n=20), goals and planning (n=14), associations (n=14), shaping knowledge (n=12), and personalization (n=7). However, we found mixed effectiveness of the techniques in mHealth outcomes, having more effective than ineffective outcomes in the evaluation of apps implementing techniques from the feedback and monitoring, goals and planning, associations, and personalization categories, but we could not infer causality with the results and suggest that there is still a need to improve the use of these and many common BCTs for better outcomes.

Conclusions

Personalization, associations, and goals and planning techniques were the most used BCTs in effective trials regarding adherence to mHealth apps. However, they are not necessarily the most effective since there are studies that use these techniques and do not report significant results in the proposed objectives; there is a notable overlap of BCTs within implemented app components, suggesting a need to better understand best practices for applying (a combination of) such techniques and to obtain details on the specific BCTs used in mHealth interventions. Future research should focus on studies with longer follow-up periods to determine the effectiveness of mHealth interventions on behavior change to overcome the limited evidence in the current literature, which has mostly small-sized and single-arm experiments with a short follow-up period.

Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review

BACKGROUND

The number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care.

OBJECTIVE

This study aims to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice.

METHODS

CINAHL, Cochrane Central, Embase, MEDLINE, and PubMed were searched to identify relevant RCTs published up to July 2021 and comparing the performance of AI-assisted tools with conventional clinical management without AI assistance. We evaluated the primary end points of each study to determine their clinical relevance. This systematic review was conducted following the updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines.

RESULTS

Among the 11,839 articles retrieved, only 39 (0.33%) RCTs were included. These RCTs were conducted in an approximately equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of gastroenterology, with 15 studies on AI-assisted endoscopy. Most RCTs studied biosignal-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools drawn from clinical data. In 77% (30/39) of the RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI-assisted intervention in 70% (21/30) of the studies. Small sample size and single-center design limited the generalizability of these studies.

CONCLUSIONS

There is growing evidence supporting the implementation of AI-assisted tools in daily clinical practice; however, the number of available RCTs is limited and heterogeneous. More RCTs of AI-assisted tools integrated into clinical practice are needed to advance the role of AI in medicine.

TRIAL REGISTRATION

PROSPERO CRD42021286539; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286539.

Influence of Sports Biomechanics on Martial Arts Sports and Comprehensive Neuromuscular Control under the Background of Artificial Intelligence

Neuromuscular control refers to the reflexes of nerves that affect muscle balance and function. In addition, there are interactions between joint structure, muscle function, and the central nervous system. In the integration with other intelligent control methods and optimization algorithms, such as fuzzy control/expert verification and genetic algorithm, it provides nonparametric object models, optimization parameters, reasoning models, and fault diagnosis. The central nervous system is the main research object of neuromuscular control. Martial arts often cause injuries or affect the progress of martial arts because of some irregular movements. Chinese traditional martial arts is another name for “martial arts” in the late Qing Dynasty in China. It is mainly reflected in the individual's application and attainments in martial arts traditional teaching methods and personal cultivation. Therefore, this paper proposes an analysis of the influence of sports biomechanics on martial arts sports and comprehensive neuromuscular control in the context of artificial intelligence. In this paper, the specific research of Wushu sports is carried out mainly in two aspects: sports biomechanics and neuromuscular control. It uses a variety of algorithms, successively using particle swarm algorithm, neural network structure, fitness function, and so on. This paper compares and analyzes their accuracy and then selects the optimal algorithm. It then conducts experimental research on the martial arts movements of professional martial arts Sanda players. The final experimental conclusion shows that, regarding lower limb selective response time and the middle left lower limb prereaction time (L-PMT) of the elite athlete group and the ordinary athlete group, the average movement value of the elite group of 2.336 is significantly greater than that of the ordinary group of 1.938. This shows that, within a certain range, the larger the knee angle and the smaller the hip angle, the stronger the ability to buffer the impact of the ground, without causing greater damage to the muscles and joints.

Artificial intelligence-based organizational human resource management and operation system

The trend of globalization, marketization, and informatization continues to strengthen, in today’s development environment, how to seize the opportunity and obtain a competitive advantage in human resources is an important issue that needs to be explored. Human resource management refers to the effective use of relevant human resources inside and outside the organization through management forms under the guidance of economics and humanistic thinking. It is a general term for a series of activities that ensure the achievement of organizational goals and the maximization of member development. With the rapid development of society and economy, the competition between enterprises has intensified. If an enterprise wants to adapt to social development, it is necessary to strengthen the internal management of the organization. The internal management also needs to rely on human resource management. The purpose of this paper is to study an organization’s human resource management and operation system based on artificial intelligence. It expects to use artificial intelligence technology to design the human resource management system and to improve the quality of employees to make the enterprise develop toward a more scientific and reasonable method. It uses artificial intelligence technology to mine the relevant data of enterprises, understand the situation of enterprises in a timely manner, and adjust unreasonable rules. This paper establishes a dynamic capability evaluation model and an early warning model for human resource management and further studies the improvement approach based on human resource management. This paper analyzes the application, feasibility, and practical significance of data mining technology in human resource management systems. It focuses on the commonly used algorithms in the field of data mining and proposes specific algorithm application scenarios and implementation ideas combined with the needs of human resource management practices. The experimental results of this paper show that the average working life of incumbent employees is 3.5 years, the average length of employees who leave the company is 5 years, and some employees are 5–6 years old. From this data, it can be seen that the average number of years of on-the-job employees is short, and the work experience has yet to be accumulated.

Digital directly observed therapy to monitor adherence to medications: a scoping review

Background: Technology-based directly observed therapy (DOT) is more cost-effective and efficient compared with in-person monitoring visits for medication adherence. While some evidence shows these technologies are feasible and acceptable, there is limited evidence collating information across medical conditions or in the context of HIV prevention, care, and treatment.Objectives: We conducted a scoping review to understand the current evidence on the acceptability, feasibility, and efficacy of digital DOT to improve medication adherence and, specifically, to determine if digital DOT had been used to improve adherence for HIV prevention, care, and treatmentMethods: We searched the electronic databases PubMed, Embase, and the Web of Science in January 2021 for any published studies with terms related to digital technologies and DOT. We included peer-reviewed studies in any population, from any country, for any outcome, and excluded conference abstracts. We included three types of digital DOT interventions: synchronous DOT, asynchronous DOT, and automated DOT. We provide an assessment of the current evidence, gaps in literature, and opportunities for intervention development regarding the use digital DOT to improve antiretroviral therapy (ART) adherence, specifically in the field of HIV.Results: We identified 28 studies that examined digital DOT. All studies found digital DOT to be acceptable and feasible. Patients using digital DOT had higher rates of treatment completion, observed doses, and adherence compared with in-person DOT, although data were limited on adherence. Only one study examined HIV prevention, and none examined ART adherence for HIV treatment.Conclusions: Digital DOT is acceptable and feasible but has not been used to remotely monitor and support ART adherence for people living with HIV.

Digital health in older adults for the prevention and management of cardiovascular diseases and frailty. A clinical consensus statement from the ESC Council for Cardiology Practice/Taskforce on Geriatric Cardiology, the ESC Digital Health Committee and the ESC Working Group on e-Cardiology

Digital health technology is receiving increasing attention in cardiology. The rise of accessibility of digital health tools including wearable technologies and smart phone applications used in medical practice has created a new era in healthcare. The coronavirus pandemic has provided a new impetus for changes in delivering medical assistance across the world. This Consensus document discusses the potential implementation of digital health technology in older adults, suggesting a practical approach to general cardiologists working in an ambulatory outpatient clinic, highlighting the potential benefit and challenges of digital health in older patients with, or at risk of, cardiovascular disease. Advancing age may lead to a progressive loss of independence, to frailty, and to increasing degrees of disability. In geriatric cardiology, digital health technology may serve as an additional tool both in cardiovascular prevention and treatment that may help by (i) supporting self-caring patients with cardiovascular disease to maintain their independence and improve the management of their cardiovascular disease and (ii) improving the prevention, detection, and management of frailty and supporting collaboration with caregivers. Digital health technology has the potential to be useful for every field of cardiology, but notably in an office-based setting with frequent contact with ambulatory older adults who may be pre-frail or frail but who are still able to live at home. Cardiologists and other healthcare professionals should increase their digital health skills and learn how best to apply and integrate new technologies into daily practice and how to engage older people and their caregivers in a tailored programme of care.

Machine learning and artificial intelligence in cardiac transplantation: A systematic review

Background

This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation.

Methods

A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021.

Results

Our search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk.

Conclusion

ML demonstrated promising applications for improving heart transplantation outcomes and patient‐centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices.

Effects of Artificial Intelligence (AI)-Assisted Dental Monitoring Intervention in Patients with Periodontitis: A Randomized Controlled Trial

AIM

To evaluate the effects of an at-home AI-assisted dental monitoring application on treatment outcomes in patients with periodontitis.

MATERIALS AND METHODS

Participants with periodontitis were recruited and randomly assigned into an AI (AI; n = 16), AI and human counseling (AIHC; n = 17), or control (CG; n = 20) group. All participants received nonsurgical periodontal treatment. We employed an AI-assisted tool called DENTAL MONITORING® (DM) intervention, a new technological AI monitoring product that utilizes smartphone cameras for intraoral scanning and assessment. Patients in the AI and AIHC groups respectively received additional (a) DM or (b) DM with real-person counseling over three months. Periodontal parameters were collected at baseline and follow-ups. A mixed-design model analyzed the follow-up effects over time.

RESULTS

The AI and AIHC groups respectively exhibited greater improvement in probing pocket depth [Mean diff = -0.9±0.4 and -1.4±0.3, effect size (ES) = 0.76 and 1.98], clinical attachment level (Mean diff = -0.8±0.3 and -1.4±0.3, ES = 0.84 and 1.77) and plaque index (Mean diff = -0.5±0.2 and -0.7±0.2, ES = 0.93 and 1.81) at 3-month follow-up than the CG did. The AIHC group had a greater reduction in probing pocket depth (ES = 0.46) and clinical attachment level (ES = 0.64) at the 3-month follow-up compared with the AI group.

CONCLUSION

Using AI monitoring at home had a positive effect on treatment outcomes for patients with periodontitis. Patients with AI-assisted health counseling exhibited better treatment outcomes than did patients who used AI monitoring alone. This article is protected by copyright. All rights reserved.

Acceptability of video observed treatment vs. directly observed treatment for tuberculosis: a comparative analysis between South and Central India

Background Directly Observed Treatment (DOT) is a requirement in the management of Tuberculosis (TB) globally. With the transition from alternate day treatment to daily treatment in India, monitoring treatment adherence through DOT is a logistic challenge. The pervasiveness of mobile phones in India provides a unique opportunity to address this challenge remotely. This study was designed to compare the acceptability of mobile phones for antitubercular treatment (ATT) support in two distinct regions of India. Methodology This was a cross-sectional exploratory study that enrolled 351 patients with TB, of whom 185 were from Bangalore, South India, and 166 from Ujjain, Central India. Trained research assistants administered a pretested questionnaire comprising demographics, phone usage patterns, and acceptability of mobile phone technology to support treatment adherence to TB medicines.  Results The mean age of the 351 participants was 32±13.6 years of whom 140 (40%) were women. Of the participants, 259 (74%) were urban, 221 (63%) had >4 years of education. A significantly greater number of participants were newly diagnosed with TB and were in the intensive phase of treatment. Overall, 218 (62%) preferred vDOT over DOT. There was an overall difference in preference between the two sites which is explained by differences in socio-economic variables. Conclusion Mobile phone adherence support is acceptable to patients on Antitubercular treatment ATT with minor variations in design based on demographic and cultural differences. In India, the preference for voice calls over text messages/SMS while designing mHealth interventions cannot be ignored. Of importance is the preference for DOT over vDOT in central India, unlike South India. However, in time, the expanding use of mobile technology supplemented with counseling, could overcome the barriers of privacy and stigma and promote the transition from in-person DOT to vDOT or mobile phone adherence monitoring and support for ATT in India.

Maintaining Stroke Care During the COVID-19 Pandemic in Lower- and Middle-Income Countries: World Stroke Organization Position Statement Endorsed by American Stroke Association and American Heart Association

For more than a year, the SARS-CoV-2 pandemic has had a devastating effect on global health. High-, low, and middle-income countries are struggling to cope with the spread of newer mutant strains of the virus. Delivery of acute stroke care remains a priority despite the pandemic. In order to maintain the time-dependent processes required to optimize delivery of intravenous thrombolysis and endovascular therapy, most countries have reorganized infrastructure to optimize human resources and critical services. Low-and-middle income countries (LMIC) have strained medical resources at baseline and often face challenges in the delivery of stroke systems of care (SSOC). This position statement aims to produce pragmatic recommendations on methods to preserve the existing SSOC during COVID-19 in LMIC and propose best stroke practices that may be low cost but high impact and commonly shared across the world.

Artificial Intelligence: Review of Current and Future Applications in Medicine

Background

The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development.

Observations

With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology.

Conclusions

AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.

Facial and Vocal Markers of Schizophrenia Measured Using Remote Smartphone Assessments: Observational Study

Background

Machine learning–based facial and vocal measurements have demonstrated relationships with schizophrenia diagnosis and severity. Demonstrating utility and validity of remote and automated assessments conducted outside of controlled experimental or clinical settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in populations that are difficult to engage.

Objective

This study aimed to determine the accuracy of machine learning–based facial and vocal measurements acquired through automated assessments conducted remotely through smartphones.

Methods

Measurements of facial and vocal characteristics including facial expressivity, vocal acoustics, and speech prevalence were assessed in 20 patients with schizophrenia over the course of 2 weeks in response to two classes of prompts previously utilized in experimental laboratory assessments: evoked prompts, where subjects are guided to produce specific facial expressions and speech; and spontaneous prompts, where subjects are presented stimuli in the form of emotionally evocative imagery and asked to freely respond. Facial and vocal measurements were assessed in relation to schizophrenia symptom severity using the Positive and Negative Syndrome Scale.

Results

Vocal markers including speech prevalence, vocal jitter, fundamental frequency, and vocal intensity demonstrated specificity as markers of negative symptom severity, while measurement of facial expressivity demonstrated itself as a robust marker of overall schizophrenia symptom severity.

Conclusions

Established facial and vocal measurements, collected remotely in schizophrenia patients via smartphones in response to automated task prompts, demonstrated accuracy as markers of schizophrenia symptom severity. Clinical implications are discussed.