ArticleCureus2025
OpenEvidence: Enhancing Medical Student Clinical Rotations With AI but With Limitations.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- The Promises and Perils of Clinical Decision Support Artificial Intelligence.The clinical teacher · 2026Article
- User Beware: Inaccuracy and Inconsistency of Large Language Models in Providing Precision Dosing Recommendations for Patients With Kidney Impairment-A Case Series.Pharmacotherapy · 2026Article
- Reference quality of OpenEvidence across five medical specialties.npj health systems · 2026Article
- Factors Shaping Trust and Satisfaction With AI Medical Chatbots: A Mixed Methods Vignette Survey of Caregivers Seeking Guidance on Pediatric Infectious Diseases.Journal of medical Internet research · 2026Article
- Assessment of Stage Two Hypertension Treatment Plans Written by Generative AI.Journal of clinical medicine · 2026Article
- Article
- The potential, perils and pitfalls of Artificial intelligence (AI) in Assisted Reproductive Technologies (ART).Reproductive biology and endocrinology : RB&E · 2026Review
- Evaluating the reference accuracy of large language models in radiology: a comparative study across subspecialties.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Observational
- Framework for AI integration into the undergraduate medical curricula: a scoping review.BMC medical education · 2026Article
- Bridging the gap between AI and traditional teaching methods in medical education: opportunities, challenges, and solutions.Frontiers in medicine · 2026Review
- Transforming surgical ward rounds: constructivist pedagogy, structured models, and intelligent technologies.Frontiers in medicine · 2026Review
- Comparative evaluation of large language models on multiple-choice and image-based rheumatology questions.Rheumatology international · 2025Article
- Efficacy of Large Language Models in Providing Evidence-Based Patient Education for Celiac Disease: A Comparative Analysis.Nutrients · 2025Article
- Comparison of responses from different artificial intelligence-powered chatbots regarding the All-on-four dental implant concept.BMC oral health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into healthcare has introduced tools that improve medical education and clinical practice. OpenEvidence is an example, providing real-time synthesis and access to medical literature, particularly for medical students during clinical rotations. By enabling efficient searches for clinical guidelines, diagnostic criteria, and therapeutic approaches, it streamlines decision-making and study preparation. Its ability to present recent publications and highlight less commonly discussed treatments supports evidence-based learning. Despite these strengths, OpenEvidence has limitations. It struggles with targeted searches for specific articles, authors, or journals and operates through an opaque curation process. Compared to ChatGPT, which offers conversational interactivity, and UpToDate, known for its comprehensive, CME-accredited content, OpenEvidence lacks certain advanced features. However, its user-friendly design and focus on clinical evidence make it a valuable, accessible alternative. This editorial critically examines OpenEvidence's capabilities and limitations, comparing it with established tools. It emphasizes the need for greater transparency, broader evidence integration, and enhanced functionality to maximize its impact. Addressing these challenges could improve OpenEvidence's utility, supporting a more effective, evidence-based approach to medical education and clinical practice.
Indexed as
Identifiers
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.