Radiologic Decision-Making for Imaging in Pulmonary Embolism: Accuracy and Reliability of Large Language Models—Bing, Claude, ChatGPT, and Perplexity

Autor:	Pradosh Kumar Sarangi, Suvrankar Datta, M. Sarthak Swarup, Swaha Panda, Debasish Swapnesh Kumar Nayak, Archana Malik, Ananda Datta, Himel Mondal
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	large language model American College of Radiology Appropriateness Criteria pulmonary embolism Bing ChatGPT Claude Perplexity Medical physics. Medical radiology. Nuclear medicine R895-920
Zdroj:	Indian Journal of Radiology and Imaging, Vol 34, Iss 04, Pp 653-660 (2024)
Druh dokumentu:	article
ISSN:	0971-3026 1998-3808
DOI:	10.1055/s-0044-1787974
Popis:	Background Artificial intelligence chatbots have demonstrated potential to enhance clinical decision-making and streamline health care workflows, potentially alleviating administrative burdens. However, the contribution of AI chatbots to radiologic decision-making for clinical scenarios remains insufficiently explored. This study evaluates the accuracy and reliability of four prominent Large Language Models (LLMs)—Microsoft Bing, Claude, ChatGPT 3.5, and Perplexity—in offering clinical decision support for initial imaging for suspected pulmonary embolism (PE).
Databáze:	Directory of Open Access Journals
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