ArticleJournal of primary care & community health
The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians.
Article in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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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
31 citing papers in PubMed.
- Large Language Model-Generated Electronic Consultations: A Blinded Comparative Study of OpenEvidence and Gastroenterologist Electronic Consultations.Mayo Clinic proceedings. Digital health · 2026Article
- The Promises and Perils of Clinical Decision Support Artificial Intelligence.The clinical teacher · 2026Article
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Web-Based, Open-Source LGBTQ+ Affirming Care Education for Primary Health Care Providers: Descriptive Analysis.Journal of medical Internet research · 2026Article
- Reference quality of OpenEvidence across five medical specialties.npj health systems · 2026Article
- Review
- Strengths and Potential Pitfalls of the Use of Artificial Intelligence in Psychiatric Education and Practice.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Artificial Intelligence in Psychiatric Graduate Medical Education.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Gut Instincts, Machine Decisions: Evaluating AI Accuracy in the Diagnosis and Treatment of Disorders of Gut-Brain Interaction.Neurogastroenterology and motility · 2026Article
- Human-AI co-design for clinical prediction models.NPJ digital medicine · 2026Article
- Utility of the Medical Knowledge AI Copilot, OpenEvidence: A Patient With 100 Cerebral Microhemorrhages.Cureus · 2026Article
- The role of large language models in the diagnosis and management of obstetric patients: a pilot feasibility study using simulated obstetric cases.Archives of gynecology and obstetrics · 2026Article
- The Role of Large Language Models in the Promotion of Minimally Invasive Interventional Radiologic Methods in Gynecology and Obstetrics.Journal of clinical medicine · 2026Article
- Assessment of Stage Two Hypertension Treatment Plans Written by Generative AI.Journal of clinical medicine · 2026Article
- Article
- Deep Research Agents: Major Breakthrough or Incremental Progress for Medical AI?Journal of medical Internet research · 2026Review
- A context-augmented large language model for accurate precision oncology medicine recommendations.Cancer cell · 2026Article
- Patterns of AI Use in Clinical Work by Hospitalists: Survey Study.Journal of medical Internet research · 2026Article
- Real World Human-LLM Interactions - Prospective blinded versus unblinded expert physician assessments of LLM responses to complex medical dilemmas.PLOS digital health · 2026Article
- ChatGPT models provide higher-quality but lower-readability responses than Google Gemini regarding anterior shoulder instability, with no added benefit of the orthopaedic expert plugin.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to address clinical questions. However, its effectiveness in real-world primary care cases remains unknown.
objectiveTo evaluate the performance of OE in providing EBM recommendations for five common chronic conditions in primary care: hypertension, hyperlipidemia, diabetes mellitus type 2, depression, and obesity.
methodsFive patient cases were retrospectively analyzed. Physicians posed specific clinical questions, and OE responses were evaluated on clarity, relevance, evidence support, impact on CDM, and overall satisfaction. Four independent physicians provided ratings using a 0 to 4 scale.
resultsOE provided accurate, evidence-based recommendations in all cases, aligning with physician plans. OE was scored on a scale of zero to four, where zero was very unclear, and four was very clear. Mean scores across cases were clarity (3.55 ± 0.60), relevance (3.75 ± 0.44), support (3.35 ± 0.49), and satisfaction (3.60 ± 0.60). However, the impact on CDM was limited (1.95 ± 1.05), as OE primarily reinforced rather than modified plans.
conclusionOE was rated high in clarity, relevance, and evidence-based support, reinforcing physician decisions in common chronic conditions. While the impact on CDM was minimal due to the study's retrospective nature, OE shows promise in augmenting the primary care physician. Prospective trials are needed to evaluate its utility in complex cases and multidisciplinary settings.
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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.