Journal article
OpenEvidence: Enhancing Medical Student Clinical Rotations With AI but With Limitations
Curēus (Palo Alto, CA), v 17(1), e76867
Jan 2025
PMID: 39897335
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.
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Details
- Title
- OpenEvidence: Enhancing Medical Student Clinical Rotations With AI but With Limitations
- Creators
- Niket Patel (Corresponding Author) - Drexel UniversityHarpreet Grewal - Florida State UniversityVenkata Buddhavarapu - Banner HealthGagandeep Dhillon - University of Maryland Medical Center
- Publication Details
- Curēus (Palo Alto, CA), v 17(1), e76867
- Publisher
- Cureus
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Medicine
- Other Identifier
- 991022201736504721