Cited by Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination

Artificial intelligence for nuclear cardiology: Perspectives and challenges DOI

C Gilbert,

Alec T Chunta, Robert J.H. Miller

et al.

International Journal of Cardiovascular Sciences, Journal Year: 2025, Volume and Issue: 38

Published: Jan. 1, 2025

Language: Английский

Citations

Taking large language models to the test: The challenge of training the next generation of nuclear cardiologists DOI

Rami Doukky,

Jonathan Tottleben

Journal of Nuclear Cardiology, Journal Year: 2025, Volume and Issue: 45, P. 102148 - 102148

Published: March 1, 2025

Language: Английский

Citations

More than pictures: The science and promise of nuclear imaging DOI

Marcelo F. Di Carli

Journal of Nuclear Cardiology, Journal Year: 2025, Volume and Issue: 45, P. 102178 - 102178

Published: March 1, 2025

Language: Английский

Citations

Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study DOI

Mohammed A. Mahyoub, Kacie Dougherty,

Ajit Shukla

et al.

JMIR Medical Informatics, Journal Year: 2025, Volume and Issue: 13, P. e67706 - e67706

Published: April 9, 2025

Background Pulmonary embolism (PE) is a critical condition requiring rapid diagnosis to reduce mortality. Extracting PE diagnoses from radiology reports manually time-consuming, highlighting the need for automated solutions. Advances in natural language processing, especially transformer models like GPT-4o, offer promising tools improve diagnostic accuracy and workflow efficiency clinical settings. Objective This study aimed develop an automatic extraction system using GPT-4o extract report impressions, enhancing decision-making efficiency. Methods In total, 2 approaches were developed evaluated: fine-tuned Clinical Longformer as baseline model GPT-4o-based extractor. Longformer, encoder-only model, was chosen its robustness text classification tasks, particularly on smaller scales. decoder-only instruction-following LLM, selected advanced understanding capabilities. The evaluate GPT-4o’s ability perform compared Longformer. trained dataset of 1000 impressions validated separate set 200 samples, while extractor same 200-sample set. Postdeployment performance further assessed additional operational records efficacy real-world setting. Results outperformed metrics, achieving sensitivity 1.0 (95% CI 1.0-1.0; Wilcoxon test, P<.001) F1-score 0.975 0.9495-0.9947; across validation dataset. evaluations also showed strong deployed with 1.0-1.0), specificity 0.94 0.8913-0.9804), 0.97 0.9479-0.9908). high level supports reduction manual review, streamlining workflows improving precision. Conclusions provides effective solution reports, offering reliable tool that aids timely accurate decision-making. approach has potential significantly patient outcomes by expediting treatment pathways conditions PE.

Language: Английский

Citations

Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination DOI

Dingyuan Luo,

Mengke Liu,

Runyuan Yu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 23, 2025

Language: Английский

Citations