Leveraging evaluation of quality on medical education research with ChatGPT
Bibliographic Data
| ID | 21447939 |
|---|---|
| Authors | Javier Alejandro Flores-Cohaila (0000-0002-9169-2094, Grupo de Investigación en Healthcare Simulation & Medical Education (HeSIM), Facultad de Ciencias de la Salud, Universidad Científica del Sur. Lima, Perú, corresponding author), Peter Garcia-Portocarrero (0000-0003-2592-7884, Universidad Nacional Pedro Ruíz Gallo), Deysi A Saldaña-Amaya (0000-0001-9564-5247, Universidad Nacional Pedro Ruíz Gallo), Brayan Miranda-Chavez (0000-0001-9035-3317, Universidad Privada de Tacna), Cesar Copaja‐Corzo (0000-0002-3497-0158, Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud, Universidad San Ignacio de Loyola) |
| Year | 2025 |
| Volume | 47 |
| Issue | 3 |
| Pages | 401-403 |
| Publication date | 2025-03-04 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Teacher (JOURNAL) |
| Journal identifiers | ISSN: 0142-159X • E-ISSN: 1466-187X |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/0142159x.2024.2385678 |
| PMID | 39099027 |
| OpenAlex | W4401346471 |
| Language | EN |
| References cited | 5 |
What is the educational challenge? The Medical Education Research Study Quality Instrument (MERSQI) is widely used to evaluate the quality of quantitative research in medical education. It has strong evidence of validity and is endorsed by guidelines. However, the manual appraisal process is time-consuming and resource-intensive, highlighting the need for more efficient methods. What are the proposed solutions? We propose to use ChatGPT to evaluate the quality of medical education research with the MERSQI and compare its scoring with those of human evaluators. What are the potential benefits to a broader global audience? Using ChatGPT to evaluate medical education research with the MERSQI can decrease the resources required for quality appraisal. This allows faster summaries of evidence, reducing the workload of researchers, editors, and educators. Furthermore, ChatGPTs’ capability to extract supporting excerpts provides transparency and may have the potential for data extraction and training new medical education researchers. What are the next steps? We plan to continue evaluating medical education research with ChatGPT using the MERSQI and other instruments to determine its feasibility in this realm. Moreover, we plan to investigate which types of studies ChatGPT performs best in
Medical education · Quality (philosophy) · Artificial Intelligence in Healthcare and Education · Healthcare cost, quality, practices · Medicine · Meta-analysis and systematic reviews · Psychology
| Citation velocity | historical |
|---|---|
| Highly cited | No |