Comparison of ChatGPT and DeepSeek large language models in the diagnosis of pericarditis.
| Title: | Comparison of ChatGPT and DeepSeek large language models in the diagnosis of pericarditis. |
|---|---|
| Authors: | Goyal A; Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, United States.; Sulaiman SA; School of Medicine, The University of Jordan, Amman 11942, Jordan.; Alaarag A; School of Medicine, The University of Jordan, Amman 11942, Jordan.; Hoshan W; School of Medicine, The University of Jordan, Amman 11942, Jordan.; Goyal P; Department of Internal Medicine, Dayanand Medical College and Hospital, Ludhiana 141001, Punjab, India.; Shah V; Department of Cardiology, Wellstar MCG Health, Augusta, GA 30912, United States.; Daoud M; Department of Internal Medicine, Bogomolets National Medical University, Kyiv 01601, Ukraine. drmohameddaoudmd@gmail.com.; Mahalwar G; Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, United States.; Sheikh AB; Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, United States. |
| Source: | World journal of cardiology [World J Cardiol] 2025 Aug 26; Vol. 17 (8), pp. 110489. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Baishideng Publishing Group Country of Publication: United States NLM ID: 101537090 Publication Model: Print Cited Medium: Print ISSN: 1949-8462 (Print) NLM ISO Abbreviation: World J Cardiol Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Publication: 2014-: Pleasanton, CA : Baishideng Publishing Group; Original Publication: Beijing : Beijing Baishideng BioMed Scientific Co. |
| Abstract: | Background: The integration of sophisticated large language models (LLMs) into healthcare has recently garnered significant attention due to their ability to leverage deep learning techniques to process vast datasets and generate contextually accurate, human-like responses. These models have been previously applied in medical diagnostics, such as in the evaluation of oral lesions. Given the high rate of missed diagnoses in pericarditis, LLMs may support clinicians in generating differential diagnoses-particularly in atypical cases where risk stratification and early identification are critical to preventing serious complications such as constrictive pericarditis and pericardial tamponade.; Aim: To compare the accuracy of LLMs in assisting the diagnosis of pericarditis as risk stratification tools.; Methods: A PubMed search was conducted using the keyword "pericarditis", applying filters for "case reports". Data from relevant cases were extracted. Inclusion criteria consisted of English-language reports involving patients aged 18 years or older with a confirmed diagnosis of acute pericarditis. The diagnostic capabilities of ChatGPT o1 and DeepThink-R1 were assessed by evaluating whether pericarditis was included in the top three differential diagnoses and as the sole provisional diagnosis. Each case was classified as either "yes" or "no" for inclusion.; Results: From the initial search, 220 studies were identified, of which 16 case reports met the inclusion criteria. In assessing risk stratification for acute pericarditis, ChatGPT o1 correctly identified the condition in 10 of 16 cases (62.5%) in the differential diagnosis and in 8 of 16 cases (50.0%) as the provisional diagnosis. DeepThink-R1 identified it in 8 of 16 cases (50.0%) and 6 of 16 cases (37.5%), respectively. ChatGPT o1 demonstrated higher accuracy than DeepThink-R1 in identifying pericarditis.; Conclusion: Further research with larger sample sizes and optimized prompt engineering is warranted to improve diagnostic accuracy, particularly in atypical presentations.; (©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.) |
| Competing Interests: | Conflict-of-interest statement: The authors declare no conflicts of interest. |
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| Contributed Indexing: | Keywords: Artificial intelligence; Cardiology; Diagnostics; Pericarditis |
| Entry Date(s): | Date Created: 20250915 Date Completed: 20250915 Latest Revision: 20250917 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC12426987 |
| DOI: | 10.4330/wjc.v17.i8.110489 |
| PMID: | 40949931 |
| Database: | MEDLINE |
Journal Article