Quality, Accuracy, and Bias in ChatGPT-Based Summarization of Medical Abstracts

CONCLUSIONS: Summaries generated by ChatGPT were 70% shorter than mean abstract length and were characterized by high quality, high accuracy, and low bias. Conversely, ChatGPT had modest ability to classify the relevance of articles to medical specialties. We suggest that ChatGPT can help family physicians accelerate review of the scientific literature and have developed software (pyJournalWatch) to support this application. Life-critical medical decisions should remain based on full, critical, and thoughtful evaluation of the full text of research articles in context with clinical guidelines.PMID:38527823 | DOI:10.1370/afm.3075

Source: Annals of Family Medicine - March 25, 2024 Category: Primary Care Authors: Joel Hake Miles Crowley Allison Coy Denton Shanks Aundria Eoff Kalee Kirmer-Voss Gurpreet Dhanda Daniel J Parente Source Type: research