ArticleJournal of family medicine and primary care2026
Artificial intelligence in clinical physiology: System-wise applications in diagnostics, monitoring, and medical education.
Article in Journal of family medicine and primary care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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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.
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Authors and funding
6 authors.
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Abstract
Background: Artificial Intelligence (AI) is gradually revolutionizing clinical physiology by enhancing diagnostic capabilities, fostering real-time monitoring, and enabling personalized medical education. Its incorporation into various physiological domains and wearable health technologies has redefined approaches to both patient care and medical training. Objective: This review seeks to systematically assess the applications of AI within the cardiovascular, respiratory, neurophysiological, and endocrine systems, as well as in physiology education. The focus is on diagnostic innovations, educational tools, ethical considerations, and emerging solutions. Methods: A comprehensive literature review was conducted utilizing PubMed, Scopus, Web of Science, and IEEE Xplore databases, covering the period from 2015 to 2025, to identify peer-reviewed studies on the application of AI in physiological diagnostics, monitoring, and competency-based medical education (CBME). Due to the heterogeneity in data types and methodologies, thematic synthesis was employed instead of meta-analysis. Results: AI has demonstrated exceptional capabilities in the interpretation of electrocardiograms and pulmonary function tests, as well as in seizure prediction, sleep analysis, diabetes management, and the enhancement of CBME tools. To address challenges such as algorithmic bias, data privacy, and accessibility, key strategies have emerged, including Explainable AI, edge computing, and federated learning frameworks. Conclusion: AI possesses the potential to revolutionize clinical physiology by facilitating predictive, personalized, and participatory healthcare. Nonetheless, its effective implementation necessitates ethical governance, inclusive datasets, and regulatory oversight to ensure equitable, transparent, and effective deployment.
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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.