{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T13:11:52Z","timestamp":1782133912047,"version":"3.54.5"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Multimodal large language models (MLLMs) integrating multiple AI systems and unimodal large language models (LLMs) represent distinct approaches to clinical decision support. Their comparative performance against human clinical experts in complex cardiovascular emergencies remains inadequately characterized. Objective: To compare the performance of a combined MLLM system (GPT-4V + Med-PaLM 2 + BioGPT), a unimodal LLM (ChatGPT-5.2), and human physicians from multiple centers (radiologists, emergency medicine specialists, cardiovascular surgeons) on aortic dissection clinical questions across diagnosis, treatment, and complication management domains. Methods: This multicenter cross-sectional study was conducted across five tertiary care centers in Turkey (Elaz\u0131\u011f, Ankara, Antalya). A total of 25 validated multiple-choice questions were categorized into three domains: diagnosis (n = 8), treatment (n = 9), and complication management (n = 8). Questions were administered to the MLLM, ChatGPT-5.2 (Unimodal), and nine physicians from five centers: radiologists (n = 3), emergency medicine specialists (n = 3), and cardiovascular surgeons (n = 3). Statistical comparisons utilized chi-square tests. Results: Overall accuracy was 92.0% for the MLLM and 96.0% for ChatGPT-5.2 (Unimodal). Among human physicians, cardiovascular surgeons achieved 96.0%, radiologists 92.0%, and emergency medicine specialists 89.3%. The MLLM excelled in diagnosis (100%) but showed lower performance in treatment (88.9%) and complication management (87.5%). No significant differences were observed between AI models and human physician groups (all p &gt; 0.05). Conclusions: Both the MLLM and unimodal ChatGPT-5.2 demonstrated performance within the range of human clinical experts in this controlled assessment of aortic dissection scenarios, though definitive conclusions regarding equivalence require larger-scale validation. These findings support further investigation of complementary roles for different AI architectures in clinical decision support.<\/jats:p>","DOI":"10.3390\/diagnostics16020323","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:35:27Z","timestamp":1768822527000},"page":"323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Comparative Performance of Multimodal and Unimodal Large Language Models Versus Multicenter Human Clinical Experts in Aortic Dissection Management"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4895-2345","authenticated-orcid":false,"given":"Evren","family":"Ekingen","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, Etimesgut Sehit Sait Erturk State Hospital, Ankara 06790, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2321-2642","authenticated-orcid":false,"given":"Mete","family":"Ucdal","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Etimesgut Sehit Sait Erturk State Hospital, Ankara 06790, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"900","DOI":"10.3348\/kjr.2025.0599","article-title":"Multimodal Large Language Models in Medical Imaging: Current State and Future Directions","volume":"26","author":"Nam","year":"2025","journal-title":"Korean J. Radiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"138547","DOI":"10.1016\/j.ijbiomac.2024.138547","article-title":"Prompt engineering-enabled LLM or MLLM and instigative bioinformatics pave the way to identify and characterize the significant SARS-CoV-2 antibody escape mutations","volume":"287","author":"Chakraborty","year":"2025","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"nwae403","DOI":"10.1093\/nsr\/nwae403","article-title":"A survey on multimodal large language models","volume":"11","author":"Yin","year":"2024","journal-title":"Natl. Sci. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1007\/s13534-025-00497-1","article-title":"From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine","volume":"15","author":"Buess","year":"2025","journal-title":"Biomed. Eng. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4999","DOI":"10.1038\/s41598-025-88948-8","article-title":"Integrated visual and text-based analysis of ophthalmology clinical cases using a large language model","volume":"15","author":"Sorin","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1038\/s41591-024-03423-7","article-title":"Toward expert-level medical question answering with large language models","volume":"31","author":"Singhal","year":"2025","journal-title":"Nat. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"bbac409","DOI":"10.1093\/bib\/bbac409","article-title":"BioGPT: Generative pre-trained transformer for biomedical text generation and mining","volume":"23","author":"Luo","year":"2022","journal-title":"Brief. Bioinform."},{"key":"ref_8","unstructured":"Wang, S., Hu, M., Li, Q., Safari, M., and Yang, X. (2025). Capabilities of gpt-5 on multimodal medical reasoning. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s41747-025-00640-8","article-title":"Automated AI detection of thoracic aortic dissection on CT imaging","volume":"9","author":"Norajitra","year":"2025","journal-title":"Eur. Radiol. Exp."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, H.H., Chang, C.B., Chen, Y.S., Kuo, C.F., Lin, C.Y., Ma, C.Y., and Wang, L.J. (2024). Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning. Diagnostics, 15.","DOI":"10.3390\/diagnostics15010012"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1016\/j.jtcvs.2023.07.046","article-title":"Perspectives on the 2022 joint guidelines on the diagnosis and management of aortic disease","volume":"166","author":"Hui","year":"2023","journal-title":"J. Thorac. Cardiovasc. Surg."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e334","DOI":"10.1161\/CIR.0000000000001106","article-title":"2022 ACC\/AHA Guideline for the Diagnosis and Management of Aortic Disease: A Report of the American Heart Association\/American College of Cardiology Joint Committee on Clinical Practice Guidelines","volume":"146","author":"Isselbacher","year":"2022","journal-title":"Circulation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e537","DOI":"10.1016\/S2589-7500(20)30218-1","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension","volume":"2","author":"Liu","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1177\/1948550617697177","article-title":"Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses","volume":"8","author":"Lakens","year":"2017","journal-title":"Soc. Psychol. Personal. Sci."},{"key":"ref_15","unstructured":"American Board of Internal Medicine (2024). ABIM Medical Knowledge Self-Assessment Program (MKSAP 19): Cardiovascular Medicine, American College of Physicians. Available online: https:\/\/mksap.acponline.org."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"q902","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e070904","DOI":"10.1136\/bmj-2022-070904","article-title":"Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"377","author":"Vasey","year":"2022","journal-title":"BMJ"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1093\/eurheartj\/ehae179","article-title":"2024 ESC Guidelines for the management of peripheral arterial and aortic diseases","volume":"45","author":"Mazzolai","year":"2024","journal-title":"Eur. Heart J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large language models in medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nat. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kung, T.H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepa\u00f1o, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., and Maningo, J. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS Digit. Health, 2.","DOI":"10.1371\/journal.pdig.0000198"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103621","DOI":"10.1016\/j.media.2025.103621","article-title":"Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications","volume":"105","author":"Schouten","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, Y., Gomez-Cabello, C.A., Haider, S.A., Genovese, A., Prabha, S., Trabilsy, M., Collaco, B.G., Wood, N.G., Bagaria, S., and Tao, C. (2025). Enhancing Clinician Trust in AI Diagnostics: A Dynamic Framework for Confidence Calibration and Transparency. Diagnostics, 15.","DOI":"10.3390\/diagnostics15172204"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e59505","DOI":"10.2196\/59505","article-title":"Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook","volume":"26","author":"AlSaad","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"AIoa2300138","DOI":"10.1056\/AIoa2300138","article-title":"Towards generalist biomedical AI","volume":"1","author":"Tu","year":"2024","journal-title":"NEJM AI"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e70080","DOI":"10.2196\/70080","article-title":"Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study","volume":"27","author":"Yang","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111132","DOI":"10.1016\/j.compbiomed.2025.111132","article-title":"Trust and accuracy in AI: Optometrists favor multimodal AI systems over unimodal for glaucoma diagnosis in collaborative environment","volume":"198","author":"Ghaffar","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Y., Yi, X., Fu, J., Yang, Y., Duan, C., and Wang, J. (2025). Reducing misdiagnosis in AI-driven medical diagnostics: A multidimensional framework for technical, ethical, and policy solutions. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1594450"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8361","DOI":"10.1038\/s41467-025-62058-5","article-title":"A foundation model for human-AI collaboration in medical literature mining","volume":"16","author":"Wang","year":"2025","journal-title":"Nat. Commun."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/2\/323\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:45:28Z","timestamp":1768823128000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/2\/323"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,19]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["diagnostics16020323"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16020323","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,19]]}}}