Menu
×
Folly Beach Library
9 a.m. - 1 p.m.
Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
West Ashley Library
9 a.m. - 4 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. - 6 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. - 6 p.m.
Phone: (843) 889-3300
Otranto Road Library
9 a.m. - 6 p.m.
Phone: (843) 572-4094
Mt. Pleasant Library
9 a.m. – 6 p.m.
Phone: (843) 849-6161
McClellanville Library
9 a.m. - 1 p.m.
Phone: (843) 887-3699
Keith Summey North Charleston Library
9 a.m. – 6 p.m.
Phone: (843) 744-2489
John's Island Library
9 a.m. - 6 p.m.
Phone: (843) 559-1945
Hurd/St. Andrews Library
9 a.m. - 6 p.m.
Phone: (843) 766-2546
Miss Jane's Building (Edisto Library Temporary Location)
9 a.m. – 3 p.m.
Phone: (843) 869-2355
Dorchester Road Library
9 a.m. - 6 p.m.
Phone: (843) 552-6466
John L. Dart Library
9 a.m. - 6 p.m.
Phone: (843) 722-7550
Baxter-Patrick James Island
9 a.m. - 6 p.m.
Phone: (843) 795-6679
Main Library
9 a.m. - 6 p.m.
Phone: (843) 805-6930
Bees Ferry West Ashley Library
9 a.m. - 6 p.m.
Phone: (843) 805-6892
Mobile Library
9 a.m. - 5 p.m.
Phone: (843) 805-6909
Today's Hours
Folly Beach Library
9 a.m. - 1 p.m.
Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
West Ashley Library
9 a.m. - 4 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. - 6 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. - 6 p.m.
Phone: (843) 889-3300
Otranto Road Library
9 a.m. - 6 p.m.
Phone: (843) 572-4094
Mt. Pleasant Library
9 a.m. – 6 p.m.
Phone: (843) 849-6161
McClellanville Library
9 a.m. - 1 p.m.
Phone: (843) 887-3699
Keith Summey North Charleston Library
9 a.m. – 6 p.m.
Phone: (843) 744-2489
John's Island Library
9 a.m. - 6 p.m.
Phone: (843) 559-1945
Hurd/St. Andrews Library
9 a.m. - 6 p.m.
Phone: (843) 766-2546
Miss Jane's Building (Edisto Library Temporary Location)
9 a.m. – 3 p.m.
Phone: (843) 869-2355
Dorchester Road Library
9 a.m. - 6 p.m.
Phone: (843) 552-6466
John L. Dart Library
9 a.m. - 6 p.m.
Phone: (843) 722-7550
Baxter-Patrick James Island
9 a.m. - 6 p.m.
Phone: (843) 795-6679
Main Library
9 a.m. - 6 p.m.
Phone: (843) 805-6930
Bees Ferry West Ashley Library
9 a.m. - 6 p.m.
Phone: (843) 805-6892
Mobile Library
9 a.m. - 5 p.m.
Phone: (843) 805-6909
Patron Login
menu
Item request has been placed!
×
Item request cannot be made.
×
![loading](/sites/all/modules/hf_eds/images/loading.gif)
Radiological Differential Diagnoses Based on Cardiovascular and Thoracic Imaging Patterns: Perspectives of Four Large Language Models.
Item request has been placed!
×
Item request cannot be made.
×
![loading](/sites/all/modules/hf_eds/images/loading.gif)
- Author(s): Sarangi, Pradosh Kumar; Irodi, Aparna; Panda, Swaha; Nayak, Debasish Swapnesh Kumar; Mondal, Himel
- Source:
Indian Journal of Radiology & Imaging; Apr2024, Vol. 34 Issue 2, p269-275, 7p- Subject Terms:
CARDIOVASCULAR system radiography; CROSS-sectional method; CONSENSUS (Social sciences); DIFFERENTIAL diagnosis; DIAGNOSTIC imaging; MEDICAL education; HEALTH; SCIENTIFIC observation; FISHER exact test; ARTIFICIAL intelligence; NATURAL language processing; CHEST X rays; INFORMATION resources; DESCRIPTIVE statistics; CHI-squared test; MATHEMATICAL models; SEARCH engines; THEORY - Source:
- Additional Information
- Subject Terms:
- Abstract: Background Differential diagnosis in radiology is a critical aspect of clinical decision-making. Radiologists in the early stages may find difficulties in listing the differential diagnosis from image patterns. In this context, the emergence of large language models (LLMs) has introduced new opportunities as these models have the capacity to access and contextualize extensive information from text-based input. Objective The objective of this study was to explore the utility of four LLMs—ChatGPT3.5, Google Bard, Microsoft Bing, and Perplexity—in providing most important differential diagnoses of cardiovascular and thoracic imaging patterns. Methods We selected 15 unique cardiovascular (n = 5) and thoracic (n = 10) imaging patterns. We asked each model to generate top 5 most important differential diagnoses for every pattern. Concurrently, a panel of two cardiothoracic radiologists independently identified top 5 differentials for each case and came to consensus when discrepancies occurred. We checked the concordance and acceptance of LLM-generated differentials with the consensus differential diagnosis. Categorical variables were compared by binomial, chi-squared, or Fisher's exact test. Results A total of 15 cases with five differentials generated a total of 75 items to analyze. The highest level of concordance was observed for diagnoses provided by Perplexity (66.67%), followed by ChatGPT (65.33%) and Bing (62.67%). The lowest score was for Bard with 45.33% of concordance with expert consensus. The acceptance rate was highest for Perplexity (90.67%), followed by Bing (89.33%) and ChatGPT (85.33%). The lowest acceptance rate was for Bard (69.33%). Conclusion Four LLMs—ChatGPT3.5, Google Bard, Microsoft Bing, and Perplexity—generated differential diagnoses had high level of acceptance but relatively lower concordance. There were significant differences in acceptance and concordance among the LLMs. Hence, it is important to carefully select the suitable model for usage in patient care or in medical education. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Indian Journal of Radiology & Imaging is the property of Thieme Medical & Scientific Publishers Private Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Contact CCPL
Copyright 2022 Charleston County Public Library Powered By EBSCO Stacks 3.3.0 [350.3] | Staff Login
No Comments.