SynthesisJMIR medical education2025
AI in the Health Sector: Systematic Review of Key Skills for Future Health Professionals.
Synthesis in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of deep machine learning in dental education: a systematic review of effectiveness in dental students' teaching learning outcomes.Evidence-based dentistry · 2026Pooled it
- Development of AI competencies within the medical curriculum.Frontiers in medicine · 2026Pooled it
- Integrating ChatGPT into knowledge-retrieval tutorials in undergraduate medical education: a prospective evaluation of higher-order learning and feasibility.Medical education online · 2026Article
- Beyond AI Literacy: Understanding Health Care Workforce AI Enablement in the Generative AI Era.Journal of medical Internet research · 2026Article
- Self-Reported Knowledge, Attitudes, Perceptions, and Readiness Regarding AI Among Obstetrics and Gynecology Trainees: Cross-Sectional Study.JMIR formative research · 2026Article
- The Scale for AI Literacy in Health Care Workers: Development and Validation.Journal of medical Internet research · 2026Article
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Artificial intelligence needs better health systems to reduce inequalities.NPJ digital medicine · 2026Review
- Artificial Intelligence in Social Health: A Narrative Review of Uses, Advantages, Challenges, and Future Directions.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence literacy, rather than artificial intelligence anxiety, is associated with career optimism in physiotherapy and rehabilitation students: a cross-sectional study.BMC medical education · 2026Article
- Cost-effectiveness of virtual patient integration in dermatology education: evidence for precision education by learner expertise.BMC medical education · 2026Article
- Agreement Between Reasoning-Oriented Generative AI Models and Clinical Educators in Evaluating Japanese Objective Structured Clinical Examination Transcripts: Preliminary Comparative Study.JMIR formative research · 2026Article
- From technological optimism to clinical realism: medical students' attitudes toward artificial ıntelligence and ıts career ımplications - a cross-sectional study at a Turkish Medical Faculty.BMC medical education · 2026Article
- AI and Its Shifting Roles in the Therapeutic Relationship: Implications for Precision Medicine.Journal of personalized medicine · 2026Review
- Re-Centering Clinical Documentation in the Age of AI Scribes: Four Aims of the Patient Chart Note.JMIR medical education · 2026Article
- AI-Assisted Formative Assessment in Clinical Education: From Algorithms to Agency.JMIR medical education · 2026Article
- An artificial intelligence application framework for nursing education: a scoping review based on the KAS model.BMC medical education · 2026Article
- Questionnaires on Perceptions of Artificial Intelligence in Health Care Among Health Care Students: Cross-Cultural Translation Into French and Linguistic Validation.JMIR medical education · 2026Article
- AI Competency: Current State and Challenges.JMIR medical education · 2026Article
- Clinical Reformation in the Age of Artificial Intelligence: Safeguarding the Ethical Centre of Medicine.Mayo Clinic proceedings. Digital health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Technological advancements have significantly reshaped health care, introducing digital solutions that enhance diagnostics and patient care. Artificial intelligence (AI) stands out, offering unprecedented capabilities in data analysis, diagnostic support, and personalized medicine. However, effectively integrating AI into health care necessitates specialized competencies among professionals, an area still in its infancy in terms of comprehensive literature and formalized training programs. Objective: This systematic review aims to consolidate the essential skills and knowledge health care professionals need to integrate AI into their clinical practice effectively, according to the published literature. Methods: We conducted a systematic review, across databases PubMed, Scopus, and Web of Science, of peer-reviewed literature that directly explored the required skills for health care professionals to integrate AI into their practice, published in English or Spanish from 2018 onward. Studies that did not refer to specific skills or training in digital health were not included, discarding those that did not directly contribute to understanding the competencies necessary to integrate AI into health care practice. Bias in the examined works was evaluated following Cochrane's domain-based recommendations. Results: The initial database search yielded a total of 2457 articles. After deleting duplicates and screening titles and abstracts, 37 articles were selected for full-text review. Out of these, only 7 met all the inclusion criteria for this systematic review. The review identified a diverse range of skills and competencies, that we categorized into 14 key areas classified based on their frequency of appearance in the selected studies, including AI fundamentals, data analytics and management, and ethical considerations. Conclusions: Despite the broadening of search criteria to capture the evolving nature of AI in health care, the review underscores a significant gap in focused studies on the required competencies. Moreover, the review highlights the critical role of regulatory bodies such as the US Food and Drug Administration in facilitating the adoption of AI technologies by establishing trust and standardizing algorithms. Key areas were identified for developing competencies among health care professionals for the implementation of AI, including: AI fundamentals knowledge (more focused on assessing the accuracy, reliability, and validity of AI algorithms than on more technical abilities such as programming or mathematics), data analysis skills (including data acquisition, cleaning, visualization, management, and governance), and ethical and legal considerations. In an AI-enhanced health care landscape, the ability to humanize patient care through effective communication is paramount. This balance ensures that while AI streamlines tasks and potentially increases patient interaction time, health care professionals maintain a focus on compassionate care, thereby leveraging AI to enhance, rather than detract from, the patient experience. .
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.