Objectives: To evaluate the performance of ChatGPT in a French medical school entrance examination.
Methods: A cross-sectional study using a consecutive sample of text-based multiple-choice practice questions for the Parcours d'Accès Spécifique Santé. ChatGPT answered questions in French. We compared performance of ChatGPT in obstetrics and gynecology (OBGYN) and in the whole test.
Results: Overall, 885 questions were evaluated. The mean test score was 34.0% (306; maximal score of 900). The performance of ChatGPT was 33.0% (292 correct answers, 885 questions). The performance of ChatGPT was lower in biostatistics (13.3% ± 19.7%) than in anatomy (34.2% ± 17.9%; P = 0.037) and also lower than in histology and embryology (40.0% ± 18.5%; P = 0.004). The OBGYN part had 290 questions. There was no difference in the test scores and the performance of ChatGPT in OBGYN versus the whole entrance test (P = 0.76 vs P = 0.10, respectively).
Conclusions: ChatGPT answered one-third of questions correctly in the French test preparation. The performance in OBGYN was similar.
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http://dx.doi.org/10.1002/ijgo.15083 | DOI Listing |
Recent advancements in large language models (LLMs) like ChatGPT and LLaMA have shown significant potential in medical applications, but their effectiveness is limited by a lack of specialized medical knowledge due to general-domain training. In this study, we developed Me-LLaMA, a new family of open-source medical LLMs that uniquely integrate extensive domain-specific knowledge with robust instruction-following capabilities. Me-LLaMA comprises foundation models (Me-LLaMA 13B and 70B) and their chat-enhanced versions, developed through comprehensive continual pretraining and instruction tuning of LLaMA2 models using both biomedical literature and clinical notes.
View Article and Find Full Text PDFPediatr Emerg Care
January 2025
University of California Davis School of Medicine, Sacramento, CA.
Objective: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mistral-Large, and Claude-3 Opus) in predicting Emergency Severity Index (ESI) levels for pediatric emergency department patients and assess the impact of medically oriented fine-tuning.
View Article and Find Full Text PDFJMIR Aging
January 2025
Department of Computing, Faculty of Computer and Mathematical Sciences, Hong Kong Polytechnic University, Hung Hom, China (Hong Kong).
Background: Providing ongoing support to the increasing number of caregivers as their needs change in the long-term course of dementia is a severe challenge to any health care system. Conversational artificial intelligence (AI) operating 24/7 may help to tackle this problem.
Objective: This study describes the development of a generative AI chatbot-the PDC30 Chatbot-and evaluates its acceptability in a mixed methods study.
J Burn Care Res
January 2025
Department of Plastic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, United States.
Patients often use Google for their medical questions. With the emergence of artificial intelligence large language models, such as ChatGPT, patients may turn to such technologies as an alternative source of medical information. This study investigates the safety, accuracy, and comprehensiveness of medical responses provided by ChatGPT in comparison to Google for common questions about burn injuries and their management.
View Article and Find Full Text PDFAnn Biomed Eng
January 2025
Department of Rehabilitation Medicine, Beijing Jishuitan Hospital, Beijing, People's Republic of China.
Background: The integration of artificial intelligence into medicine has attracted increasing attention in recent years. ChatGPT has emerged as a promising tool for delivering evidence-based recommendations in various clinical domains. However, the application of ChatGPT to physical therapy for musculoskeletal conditions has yet to be investigated.
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