SynthesisFrontiers in digital health2026
Telemedicine and artificial intelligence in family medicine practice: a systematic review and meta-analysis of barriers and enablers in routine primary care.
Synthesis in Frontiers in digital health, 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.
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.
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Who cites it
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Authors and funding
1 author.
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Abstract
Introduction: Telemedicine and artificial intelligence (AI) are reshaping healthcare delivery, yet their integration into routine family practice remains inconsistent. This systematic review and meta-analysis aimed to identify barriers and enablers influencing the adoption of telemedicine and AI in primary care and to quantify their effects on clinical and operational outcomes. Methods: This review followed PRISMA guidelines and was registered with PROSPERO (CRD420251029675). Six databases-PubMed, Scopus, Web of Science, Cochrane Library, CINAHL, and IEEE Xplore-were searched from inception to March 15, 2025. Eligible studies were randomized controlled trials examining telemedicine or AI implementation in family practice or primary care settings. Data extraction covered study characteristics, intervention type, outcomes, and reported barriers and enablers. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Narrative synthesis, thematic analysis, and random-effects meta-analysis were conducted where appropriate. Results: Thirteen randomized controlled trials involving more than 29,000 participants were included. Telemedicine interventions showed favorable study-level effects on chronic disease management, access to care, and patient satisfaction, although the pooled clinical effect across ten trials did not reach statistical significance. AI applications significantly improved diagnostic accuracy and clinical decision-making, with a pooled log odds ratio of 0.73 (95% CI: 0.46-1.00). Key enablers included clinician engagement, reliable digital infrastructure, leadership support, training, and workflow integration. Major barriers included technological interoperability, clinician skepticism, patient digital literacy limitations, privacy concerns, and uncertain reimbursement or governance frameworks. Discussion: Telemedicine and AI have promising roles in strengthening family practice, particularly for access, chronic disease management, diagnostic support, and decision-making. Sustainable implementation requires attention to technological, organizational, regulatory, and human factors, alongside targeted training, policy support, and integration into routine primary care workflows.
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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.