Digital health competences and AI beliefs as conditions for the practice of evidence-based medicine: a study of prospective physicians in Canada

BACKGROUND

The practice of evidence-based medicine (EBM) has become pivotal in enhancing medical care and patient outcomes. With the diffusion of innovation in healthcare organizations, EBM can be expected to depend on medical professionals' competences with digital health (dHealth) and artificial intelligence (AI) technologies.

OBJECTIVE

We aim to investigate the effect of dHealth competences and perceptions of AI on the adoption of EBM among prospective physicians. By focusing on dHealth and AI technologies, the study seeks to inform the redesign of medical curricula to better prepare students for the demands of evidence-based medical practice.

METHODS

A cross-sectional survey was administered online to students at the University of Montreal's medical school, which has approximately 1,400 enrolled students. The survey included questions on students' dHealth competences, perceptions of AI, and their practice of EBM. Using structural equation modeling (SEM), we analyzed data from 177 respondents to test our research model.

RESULTS

Our analysis indicates that medical students possess foundational knowledge competences of dHealth technologies and perceive AI to play an important role in the future of medicine. Yet, their experiential competences with dHealth technologies are limited. Our findings reveal that experiential dHealth competences are significantly related to the practice of EBM (β = 0.42, p < 0.001), as well as students' perceptions of the role of AI in the future of medicine (β = 0.39, p < 0.001), which, in turn, also affect EBM (β = 0.19, p < 0.05).

CONCLUSIONS

The study underscores the necessity of enhancing students' competences related to dHealth and considering their perceptions of the role of AI in the medical profession. In particular, the low levels of experiential dHealth competences highlight a promising starting point for training future physicians while simultaneously strengthening their practice of EBM. Accordingly, we suggest revising medical curricula to focus on providing students with practical experiences with dHealth and AI technologies.

A keyword-based approach to analyzing scientific research trends: ReRAM present and future

Research trend analysis is a primary step in defining research structures and predicting research directions from scientific papers. Recently, due to millions of annual scientific publications, researchers demand analytical methods to interpret the research field topologically and temporally. In this study, we propose a keyword-based research trend analysis method that automatically and systematically analyzes the research field by extracting keywords and constructing a keyword network. We verified our method on the resistive random-access memory (ReRAM) research field, which is in the limelight as an alternative device for non-volatile memory and artificial synapses. Our method performs three sequential processes: article collection, keyword extraction, and research structuring. We identified three keyword communities of ReRAM based on the processing-structure-property-performance (PSPP) relationship and found an upward trend in Neuromorphic applications. As a result, our method successfully structures the ReRAM research field and is expected to provide detailed insights into various research fields.

An Academic Viewpoint (2025) on the Integration of Generative Artificial Intelligence in Medical Education: Transforming Learning and Practices

Generative artificial intelligence (GAI) has introduced a new era of medical education by offering innovative solutions to critical challenges in teaching, assessment, and clinical training. This expanded review explores the current and potential applications of GAI across multiple domains, including personalized tutoring, enhanced academic administrative efficiency, and improved preparedness for daily learning interactions. Utilizing a narrative review methodology combined with expert analysis, this study involved a structured literature search in January 2025 across PubMed, Scopus, and Google Scholar, followed by iterative brainstorming sessions and expert evaluations to assess the feasibility and impact of various GAI applications. Six domain experts then appraised the feasibility and impact of GAI technologies across educational settings, resulting in 10 identified domains of application: Quality and Administration, Curriculum Development, Teaching and Learning, Assessment and Evaluation, Clinical Training, Academic Guidance, Student Research, Student Affairs, Internship Management, and Student Activities. Our findings highlight how GAI supports personalized learning - through adaptive tutoring and automated performance dashboards - while optimizing administrative tasks such as course registration and policy oversight. In addition, immersive simulations and virtual patient encounters reinforce clinical decision-making and practical skills. GAI-driven tools also streamline research processes via automated literature reviews and proposal refinement, ultimately fostering greater efficiency across academic environments. Despite these opportunities, ethical considerations remain a priority. Issues pertaining to data privacy, algorithmic bias, and equitable access must be addressed through robust regulatory frameworks and institution-wide policies. Overall, by embracing targeted, ethically guided implementations, GAI has the evolving potential to enhance educational quality, improve operational effectiveness, and equip future healthcare professionals with the adaptive skills needed in a patient-centered clinical landscape.

Methodological components, structure, and quality assessment tools for evidence summaries: a scoping review

OBJECTIVE

The objective of this review was to identify and map the available information related to the definition, structure, and core methodological components of evidence summaries, as well as to identify any indicators of quality.

INTRODUCTION

Evidence summaries offer a practical solution to overcoming some of the barriers present in evidence-based health care, such as lack of access to evidence at the point of care, and the knowledge and expertise to evaluate the quality and translate the evidence into clinical decision-making. However, lack of transparency in reporting and inconsistencies in the methodology of evidence summary development have previously been cited and pose problems for end users (eg, clinicians, policymakers).

INCLUSION CRITERIA

Any English-language resource that described the methodological development or appraisal of an evidence summary was included.

METHODS

PubMed, Embase, and CINAHL (EBSCOhost) were systematically searched in November 2019, with no limits on the search. The search was updated in June 2021 and January 2023. Gray literature searches and pearling of references of included sources were also conducted at the same time as the database searches. All resources (ie, articles, papers, books, dissertations, reports, and websites) were eligible for inclusion in the review if they evaluated or described the development or appraisal of an evidence summary methodology within a point-of-care context and were published in English. Literature reviews (eg, systematic reviews, rapid reviews)-including summaries of evidence on interventions or health care activities that measure effects, a phenomenon of interest, or where the objective was the development, description, or evaluation of methods without a clear point-of-care target-were excluded from the review.

RESULTS

A total of 76 resources (n = 56 articles from databases and n = 20 reports from gray literature sources) were included in the review. The most common type/name of resource included critically appraised topic (n = 18) and evidence summary (n = 17). A total of 25 resources provided a definition of an evidence summary: commonalities included a clinical question; a structured, systematic literature search; a description of literature selection; and appraisal of evidence. Of these 25 resources, 16 included descriptors such as brief, concise, rapid, short, succinct , and snapshot . The reported methodological components closely reflected the definition results, with the most reported methodological components being a systematic, multi-database search, and critical appraisal. Evidence summary examples were mostly presented as narrative summaries and usually included a reference list, background or clinical context, and recommendations or implications for practice or policy. Four quality assessment tools and a systematic review of tools were included.

CONCLUSIONS

The findings of this review highlight the wide variability in the definition, language, methodological components, and structure used for point-of-care resources that met our definition of an evidence summary. This scoping review is one of the first stjpg aimed at improving the credibility and transparency of evidence summaries in evidence-based health care, with further research required to standardize the definitions and methodologies associated with point-of-care resources and accepted tools for quality assessment.

SUPPLEMENTAL DIGITAL CONTENT

A Chinese-language version of the abstract of this review is available at http://links.lww.com/SRX/A79 ; a list of studies ineligible following full-text review is available at http://links.lww.com/SRX/A60 .

Limited consensus on what climate anxiety is: Insights from content overlap analysis on 12 questionnaires

Climate anxiety is a phenomenon that is gaining importance due to the general public's increased awareness of the worsening climate crisis. At present, climate anxiety is not operationalized consistently across the existing literature. It is important to gain more consensus on the definition and operationalization of climate anxiety to facilitate reliable and generalizable research and to further develop interventions. Content analysis can contribute to this by providing insight into the overlap in the content of climate anxiety measures. With a systematic search, this study identified and analyzed 12 distinct scales measuring climate anxiety. The 119 items covered a total of 57 disparate symptoms. Jaccard indices showed that the mean overlap between symptoms of different climate anxiety scales was generally very low, as was the overlap between pairwise comparisons of climate anxiety scales. These results highlight the lack of uniformity in assessing climate anxiety and the need to properly define and operationalize this concept. The potential reasons for low overlap and how this might impact the reliability and validity of existing measures are discussed. It is critical that future work aims at finding consensus on the definition of climate anxiety (e.g., through a Delphi study) and psychometrically comparing the different questionnaires.

Use of large language models as artificial intelligence tools in academic research and publishing among global clinical researchers

With breakthroughs in Natural Language Processing and Artificial Intelligence (AI), the usage of Large Language Models (LLMs) in academic research has increased tremendously. Models such as Generative Pre-trained Transformer (GPT) are used by researchers in literature review, abstract screening, and manuscript drafting. However, these models also present the attendant challenge of providing ethically questionable scientific information. Our study provides a snapshot of global researchers' perception of current trends and future impacts of LLMs in research. Using a cross-sectional design, we surveyed 226 medical and paramedical researchers from 59 countries across 65 specialties, trained in the Global Clinical Scholars' Research Training certificate program of Harvard Medical School between 2020 and 2024. Majority (57.5%) of these participants practiced in an academic setting with a median of 7 (2,18) PubMed Indexed published articles. 198 respondents (87.6%) were aware of LLMs and those who were aware had higher number of publications (p < 0.001). 18.7% of the respondents who were aware (n = 37) had previously used LLMs in publications especially for grammatical errors and formatting (64.9%); however, most (40.5%) did not acknowledge its use in their papers. 50.8% of aware respondents (n = 95) predicted an overall positive future impact of LLMs while 32.6% were unsure of its scope. 52% of aware respondents (n = 102) believed that LLMs would have a major impact in areas such as grammatical errors and formatting (66.3%), revision and editing (57.2%), writing (57.2%) and literature review (54.2%). 58.1% of aware respondents were opined that journals should allow for use of AI in research and 78.3% believed that regulations should be put in place to avoid its abuse. Seeing the perception of researchers towards LLMs and the significant association between awareness of LLMs and number of published works, we emphasize the importance of developing comprehensive guidelines and ethical framework to govern the use of AI in academic research and address the current challenges.

Deep learning methods for 3D magnetic resonance image denoising, bias field and motion artifact correction: a comprehensive review

Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufacturers incorporate various post-acquisition image-processing techniques into the scanner's computer software tools for different post-processing tasks. These tools provide a final image of adequate quality and essential features for accurate clinical reporting and predictive interpretation for better treatment planning. Different post-acquisition image-processing tasks for MRI quality enhancement include noise removal, motion artifact reduction, magnetic bias field correction, and eddy electric current effect removal. Recently, deep learning (DL) methods have shown great success in many research fields, including image and video applications. DL-based data-driven feature-learning approaches have great potential for MR image denoising and image-quality-degrading artifact correction. Recent studies have demonstrated significant improvements in image-analysis tasks using DL-based convolutional neural network techniques. The promising capabilities and performance of DL techniques in various problem-solving domains have motivated researchers to adapt DL methods to medical image analysis and quality enhancement tasks. This paper presents a comprehensive review of DL-based state-of-the-art MRI quality enhancement and artifact removal methods for regenerating high-quality images while preserving essential anatomical and physiological feature maps without destroying important image information. Existing research gaps and future directions have also been provided by highlighting potential research areas for future developments, along with their importance and advantages in medical imaging.

Ethical challenges for Health Technology Assessment (HTA) in the evolving evidence landscape

Since its inception, Health Technology Assessment (HTA) has typically determined the value of a technology by collecting information derived from randomized clinical trials (RCTs), in line with the principles of evidence-based medicine (EBM). However, data from RCTs did not constitute the sole source of information, as other types of evidence (such as primary qualitative research) have often been utilized. Recent advances in both generating and collecting other types of evidence are broadening the landscape of evidence, adding complexity to the discussion of "robustness of evidence." What are the consequences of these recent developments for the methodology and conduct of HTA, the HTA community, and its ethical commitments? The aim of this article is to explore some ethical challenges that are emerging in the current evolving evidence landscape, particularly changes in evidence generation and collection (e.g., diversification of data sources), and shifting standards of evidence in the field of HTA (e.g., increasing acceptability of evidence that is thought of as lower quality). Our conclusion is that deciding how to best maintain trustworthiness is common to all these issues.

(Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review

Background

An abundance of rapidly accumulating scientific evidence presents novel opportunities for researchers and practitioners alike, yet such advantages are often overshadowed by resource demands associated with finding and aggregating a continually expanding body of scientific information. Data extraction activities associated with evidence synthesis have been described as time-consuming to the point of critically limiting the usefulness of research. Across social science disciplines, the use of automation technologies for timely and accurate knowledge synthesis can enhance research translation value, better inform key policy development, and expand the current understanding of human interactions, organizations, and systems. Ongoing developments surrounding automation are highly concentrated in research for evidence-based medicine with limited evidence surrounding tools and techniques applied outside of the clinical research community. The goal of the present study is to extend the automation knowledge base by synthesizing current trends in the application of extraction technologies of key data elements of interest for social scientists.

Methods

We report the baseline results of a living systematic review of automated data extraction techniques supporting systematic reviews and meta-analyses in the social sciences. This review follows PRISMA standards for reporting systematic reviews.

Results

The baseline review of social science research yielded 23 relevant studies.

Conclusions

When considering the process of automating systematic review and meta-analysis information extraction, social science research falls short as compared to clinical research that focuses on automatic processing of information related to the PICO framework. With a few exceptions, most tools were either in the infancy stage and not accessible to applied researchers, were domain specific, or required substantial manual coding of articles before automation could occur. Additionally, few solutions considered extraction of data from tables which is where key data elements reside that social and behavioral scientists analyze.

Machine learning to optimize literature screening in medical guideline development

OBJECTIVES

In a time of exponential growth of new evidence supporting clinical decision-making, combined with a labor-intensive process of selecting this evidence, methods are needed to speed up current processes to keep medical guidelines up-to-date. This study evaluated the performance and feasibility of active learning to support the selection of relevant publications within medical guideline development and to study the role of noisy labels.

DESIGN

We used a mixed-methods design. Two independent clinicians' manual process of literature selection was evaluated for 14 searches. This was followed by a series of simulations investigating the performance of random reading versus using screening prioritization based on active learning. We identified hard-to-find papers and checked the labels in a reflective dialogue.

MAIN OUTCOME MEASURES

Inter-rater reliability was assessed using Cohen's Kappa (ĸ). To evaluate the performance of active learning, we used the Work Saved over Sampling at 95% recall (WSS@95) and percentage Relevant Records Found at reading only 10% of the total number of records (RRF@10). We used the average time to discovery (ATD) to detect records with potentially noisy labels. Finally, the accuracy of labeling was discussed in a reflective dialogue with guideline developers.

RESULTS

Mean ĸ for manual title-abstract selection by clinicians was 0.50 and varied between - 0.01 and 0.87 based on 5.021 abstracts. WSS@95 ranged from 50.15% (SD = 17.7) based on selection by clinicians to 69.24% (SD = 11.5) based on the selection by research methodologist up to 75.76% (SD = 12.2) based on the final full-text inclusion. A similar pattern was seen for RRF@10, ranging from 48.31% (SD = 23.3) to 62.8% (SD = 21.20) and 65.58% (SD = 23.25). The performance of active learning deteriorates with higher noise. Compared with the final full-text selection, the selection made by clinicians or research methodologists deteriorated WSS@95 by 25.61% and 6.25%, respectively.

CONCLUSION

While active machine learning tools can accelerate the process of literature screening within guideline development, they can only work as well as the input given by human raters. Noisy labels make noisy machine learning.

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare

Background

Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this.

Challenges with the three components of EBP

Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources.

AI assistance for the three components of EBP

AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue.

Conclusion

This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.

Prescribing and deprescribing guidance for benzodiazepine and benzodiazepine receptor agonist use in adults with depression, anxiety, and insomnia: an international scoping review

Background

Clinical practice guidelines and guidance documents routinely offer prescribing clinicians' recommendations and instruction on the use of psychotropic drugs for mental illness. We sought to characterise parameters relevant to prescribing and deprescribing of benzodiazepine (BZD) and benzodiazepine receptor agonist (BZRA), in clinical practice guidelines and guidance documents internationally, for adult patients with unipolar depression, anxiety disorders and insomnia to understand similarities and discrepancies between evidence-based expert opinion.

Methods

A Scoping Review was conducted to characterize documents that offered evidence-based and/or consensus pharmacologic guidance on the management of unipolar depression, anxiety disorders, obsessive-compulsive disorders, post-traumatic stress disorders and insomnia. A systematic search was conducted of PubMed, SCOPUS, PsycINFO and CINAHL from inception to October 13, 2023 and supplemented by a gray literature search. Documents were screened in Covidence for eligibility. Subsequent data-charting on eligible documents collected information on aspects of both prescribing and deprescribing.

Findings

113 documents offering guidance on BZD/BZRA use were data-charted. Overall, documents gathered were from Asia (n = 11), Europe (n = 34), North America (n = 37), Oceania (n = 7), and South America (n = 4) with the remainder being "International" (n = 20) and not representative to any particular region or country. By condition the documents reviewed covered unipolar depressive disorders (n = 28), anxiety disorders, obsessive-compulsive disorder and post-traumatic stress disorder (n = 42) and Insomnia (n = 25). Few documents (n = 18) were sufficiently specific and complete to consider as de-prescribing focused documents.

Interpretation

Documents were in concordance in terms of BZD and BZRA not being used routinely as first-line pharmacologic agents. When used, it is advisable to restrict their duration to "short-term" use with the most commonly recommended duration being less than four weeks. Documents were less consistent in terms of prescriptive recommendations for specific drug, dosing and administration pattern (i.e regular or 'as needed') selection for each condition. Deprescribing documents were unanimously in favor of gradual dose reduction and patient shared decision-making. However, approaches towards dose-tapering differed substantially. Finally, there were inconsistencies and/or insufficiency of detail, among deprescribing documents, in terms of switching to a long-acting BZD, use of adjunctive pharmacotherapies and micro-tapering.

Funding

The authors received no funding for this work.

Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It

With the rapid advancement of artificial intelligence (AI), the field of infectious diseases (ID) faces both innovation and disruption. AI and its subfields including machine learning, deep learning, and large language models can support ID clinicians' decision making and streamline their workflow. AI models may help ensure earlier detection of disease, more personalized empiric treatment recommendations, and allocation of human resources to support higher-yield antimicrobial stewardship and infection prevention strategies. AI is unlikely to replace the role of ID experts, but could instead augment it. However, its limitations will need to be carefully addressed and mitigated to ensure safe and effective implementation. ID experts can be engaged in AI implementation by participating in training and education, identifying use cases for AI to help improve patient care, designing, validating and evaluating algorithms, and continuing to advocate for their vital role in patient care.

Artificial intelligence and illusions of understanding in scientific research

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists' visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community's ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions

Subsurface interpretations and models rely on knowledge from subject matter experts who utilize unstructured information from images, maps, cross sections, and other products to provide context to measured data (e. g., cores, well logs, seismic surveys). To enhance such knowledge discovery, we advanced the National Energy Technology Laboratory's (NETL) Subsurface Trend Analysis (STA) workflow with an artificial intelligence (AI) deep learning approach for image embedding. NETL's STA method offers a validated science-based approach of combining geologic systems knowledge, statistical modeling, and datasets to improve predictions of subsurface properties. The STA image embedding tool quickly extracts images from unstructured knowledge products like publications, maps, websites, and presentations; categorically labels the images; and creates a repository for geologic domain postulation. Via a case study on geographic and subsurface literature of the Gulf of Mexico (GOM), results show the STA image embedding tool extracts images and correctly labels them with ~90 to ~95% accuracy.

Machine culture

The ability of humans to create and disseminate culture is often credited as the single most important factor of our success as a species. In this Perspective, we explore the notion of 'machine culture', culture mediated or generated by machines. We argue that intelligent machines simultaneously transform the cultural evolutionary processes of variation, transmission and selection. Recommender algorithms are altering social learning dynamics. Chatbots are forming a new mode of cultural transmission, serving as cultural models. Furthermore, intelligent machines are evolving as contributors in generating cultural traits-from game strategies and visual art to scientific results. We provide a conceptual framework for studying the present and anticipated future impact of machines on cultural evolution, and present a research agenda for the study of machine culture.

Expanding horizons and navigating challenges for enhanced clinical workflows: ChatGPT in urology

Purpose of review

ChatGPT has emerged as a potential tool for facilitating doctors' workflows. However, when it comes to applying these findings within a urological context, there have not been many studies. Thus, our objective was rooted in analyzing the pros and cons of ChatGPT use and how it can be exploited and used by urologists.

Recent findings

ChatGPT can facilitate clinical documentation and note-taking, patient communication and support, medical education, and research. In urology, it was proven that ChatGPT has the potential as a virtual healthcare aide for benign prostatic hyperplasia, an educational and prevention tool on prostate cancer, educational support for urological residents, and as an assistant in writing urological papers and academic work. However, several concerns about its exploitation are presented, such as lack of web crawling, risk of accidental plagiarism, and concerns about patients-data privacy.

Summary

The existing limitations mediate the need for further improvement of ChatGPT, such as ensuring the privacy of patient data and expanding the learning dataset to include medical databases, and developing guidance on its appropriate use. Urologists can also help by conducting studies to determine the effectiveness of ChatGPT in urology in clinical scenarios and nosologies other than those previously listed.

Knowledge in Motion: A Comprehensive Review of Evidence-Based Human Kinetics

This comprehensive review examines critical aspects of evidence-based human kinetics, focusing on bridging the gap between scientific evidence and practical implementation. To bridge this gap, the development of tailored education and training programs is essential, providing practitioners with the expertise and skills to effectively apply evidence-based programs and interventions. The effectiveness of these programs in improving physical fitness across all age groups has been widely demonstrated. In addition, integrating artificial intelligence and the principles of slow science into evidence-based practice promises to identify gaps in knowledge and stimulate further research in human kinetics. The purpose of this review is to provide researchers and practitioners with comprehensive information on the application of scientific principles in human kinetics. By highlighting the importance of evidence-based practice, this review is intended to promote the adoption of effective interventions to optimize physical health and enhance performance.

Artificial Intelligence Applied to Improve Scientific Reviews: The Antibacterial Activity of Cistus Plants as Proof of Concept

Reviews have traditionally been based on extensive searches of the available bibliography on the topic of interest. However, this approach is frequently influenced by the authors' background, leading to possible selection bias. Artificial intelligence applied to natural language processing (NLP) is a powerful tool that can be used for systematic reviews by speeding up the process and providing more objective results, but its use in scientific literature reviews is still scarce. This manuscript addresses this challenge by developing a reproducible tool that can be used to develop objective reviews on almost every topic. This tool has been used to review the antibacterial activity of Cistus genus plant extracts as proof of concept, providing a comprehensive and objective state of the art on this topic based on the analysis of 1601 research manuscripts and 136 patents. Data were processed using a publicly available Jupyter Notebook in Google Collaboratory here. NLP, when applied to the study of antibacterial activity of Cistus plants, is able to recover the main scientific manuscripts and patents related to the topic, avoiding any biases. The NLP-assisted literature review reveals that C. creticus and C. monspeliensis are the first and second most studied Cistus species respectively. Leaves and fruits are the most commonly used plant parts and methanol, followed by butanol and water, the most widely used solvents to prepare plant extracts. Furthermore, Staphylococcus. aureus followed by Bacillus. cereus are the most studied bacterial species, which are also the most susceptible bacteria in all studied assays. This new tool aims to change the actual paradigm of the review of scientific literature to make the process more efficient, reliable, and reproducible, according to Open Science standards.

Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment

Background

Artificial intelligence (AI) has attracted much attention because of its enormous potential in healthcare, but uptake has been slow. There are substantial barriers that challenge health technology assessment (HTA) professionals to use AI-generated evidence for decision-making from large real-world databases (e.g., based on claims data). As part of the European Commission-funded HTx H2020 (Next Generation Health Technology Assessment) project, we aimed to put forward recommendations to support healthcare decision-makers in integrating AI into the HTA processes. The barriers, addressed by the paper, are particularly focusing on Central and Eastern European (CEE) countries, where the implementation of HTA and access to health databases lag behind Western European countries.

Methods

We constructed a survey to rank the barriers to using AI for HTA purposes, completed by respondents from CEE jurisdictions with expertise in HTA. Using the results, two members of the HTx consortium from CEE developed recommendations on the most critical barriers. Then these recommendations were discussed in a workshop by a wider group of experts, including HTA and reimbursement decision-makers from both CEE countries and Western European countries, and summarized in a consensus report.

Results

Recommendations have been developed to address the top 15 barriers in areas of (1) human factor-related barriers, focusing on educating HTA doers and users, establishing collaborations and best practice sharing; (2) regulatory and policy-related barriers, proposing increasing awareness and political commitment and improving the management of sensitive information for AI use; (3) data-related barriers, suggesting enhancing standardization and collaboration with data networks, managing missing and unstructured data, using analytical and statistical approaches to address bias, using quality assessment tools and quality standards, improving reporting, and developing better conditions for the use of data; and (4) technological barriers, suggesting sustainable development of AI infrastructure.

Conclusion

In the field of HTA, the great potential of AI to support evidence generation and evaluation has not yet been sufficiently explored and realized. Raising awareness of the intended and unintended consequences of AI-based methods and encouraging political commitment from policymakers is necessary to upgrade the regulatory and infrastructural environment and knowledge base required to integrate AI into HTA-based decision-making processes better.

Objectivization of virus titration method in GMP-regulated environment using artificial intelligence-based classification system

The implementation of artificial intelligence-based systems (AI) into highly regulated industries faces significant regulatory and logistical challenges. The lack of established practices and guidelines, risk aversion attitude, perception of AI technology as insufficiently tested and unreliable are among the most important factors preventing wider adoption of AI solutions into pharmaceutical industry. Here we demonstrate by an example development and validation of neural network-based computer vision systems for binary classification of results from virus titration assays often used in research and production of vaccines and antivirals. The systems were shown to effectively classify images of cells from the titration assays to negative (non-infected) or positive (virus infected) with an accuracy of over 99%. The methods have been validated according to GMP principles and related guidelines. Regulatory and logistical challenges of implementation of AI-based solutions into GMP-compliant systems are discussed. Although the implementation of AI-systems into GMP-regulated environment brings specific challenges related to introduction of novel technologies, future benefits for early adopters could outweigh the initial efforts.

Development of benchmark datasets for text mining and sentiment analysis to accelerate regulatory literature review

In the field of regulatory science, reviewing literature is an essential and important step, which most of the time is conducted by manually reading hundreds of articles. Although this process is highly time-consuming and labor-intensive, most output of this process is not well transformed into machine-readable format. The limited availability of data has largely constrained the artificial intelligence (AI) system development to facilitate this literature reviewing in the regulatory process. In the past decade, AI has revolutionized the area of text mining as many deep learning approaches have been developed to search, annotate, and classify relevant documents. After the great advancement of AI algorithms, a lack of high-quality data instead of the algorithms has recently become the bottleneck of AI system development. Herein, we constructed two large benchmark datasets, Chlorine Efficacy dataset (CHE) and Chlorine Safety dataset (CHS), under a regulatory scenario that sought to assess the antiseptic efficacy and toxicity of chlorine. For each dataset, ∼10,000 scientific articles were initially collected, manually reviewed, and their relevance to the review task were labeled. To ensure high data quality, each paper was labeled by a consensus among multiple experienced reviewers. The overall relevance rate was 27.21% (2,663 of 9,788) for CHE and 7.50% (761 of 10,153) for CHS, respectively. Furthermore, the relevant articles were categorized into five subgroups based on the focus of their content. Next, we developed an attention-based classification language model using these two datasets. The proposed classification model yielded 0.857 and 0.908 of Area Under the Curve (AUC) for CHE and CHS dataset, respectively. This performance was significantly better than permutation test (p < 10E-9), demonstrating that the labeling processes were valid. To conclude, our datasets can be used as benchmark to develop AI systems, which can further facilitate the literature review process in regulatory science.

Extending artificial intelligence research in the clinical domain: a theoretical perspective

Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.