{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:40:43Z","timestamp":1792687243081,"version":"4.5.0"},"reference-count":50,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T00:00:00Z","timestamp":1746835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Background\/Objectives: Large language models (LLMs), such as ChatGPT, have emerged as potential clinical support tools to enhance precision in personalized patient care, but their reliability in radiological image interpretation remains uncertain. The primary aim of our study was to evaluate the diagnostic accuracy of ChatGPT-4o in interpreting chest X-rays (CXRs) and abdominal X-rays (AXRs) by comparing its performance to expert radiology findings, whilst secondary aims were diagnostic confidence and patient safety. Methods: A total of 500 X-rays, including 257 CXR (51.4%) and 243 AXR (48.5%), were analyzed. Diagnoses made by ChatGPT-4o were compared to expert interpretations. Confidence scores (1\u20134) were assigned and responses were evaluated for patient safety. Results: ChatGPT-4o correctly identified 345 of 500 (69%) pathologies (95% CI: 64.81\u201372.9). For AXRs 175 of 243 (72.02%) pathologies were correctly diagnosed (95% CI: 66.06\u201377.28), while for CXRs 170 of 257 (66.15%) were accurate (95% CI: 60.16\u201371.66). The highest detection rates among CXRs were observed for pulmonary edema, tumor, pneumonia, pleural effusion, cardiomegaly, and emphysema, and lower rates were observed for pneumothorax, rib fractures, and enlarged mediastinum. AXR performance was highest for intestinal obstruction and foreign bodies, and weaker for pneumoperitoneum, renal calculi, and diverticulitis. Confidence scores were higher for AXRs (mean 3.45 \u00b1 1.1) than CXRs (mean 2.48 \u00b1 1.45). All responses (100%) were considered to be safe for the patient. Interobserver agreement was high (kappa = 0.920), and reliability (second prompt) was moderate (kappa = 0.750). Conclusions: ChatGPT-4o demonstrated moderate accuracy for the interpretation of X-rays, being higher for AXRs compared to CXRs. Improvements are required for its use as efficient clinical support tool.<\/jats:p>","DOI":"10.3390\/jpm15050194","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T06:17:08Z","timestamp":1747030628000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["The Accuracy of ChatGPT-4o in Interpreting Chest and Abdominal X-Ray Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4049-4494","authenticated-orcid":false,"given":"Pietro G.","family":"Lacaita","sequence":"first","affiliation":[{"name":"Department Radiology, Innsbruck Medical University, 6020 Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6577-0784","authenticated-orcid":false,"given":"Malik","family":"Galijasevic","sequence":"additional","affiliation":[{"name":"Department Radiology, Innsbruck Medical University, 6020 Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Swoboda","sequence":"additional","affiliation":[{"name":"Department Radiology, Innsbruck Medical University, 6020 Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonhard","family":"Gruber","sequence":"additional","affiliation":[{"name":"Department Radiology, Innsbruck Medical University, 6020 Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yannick","family":"Scharll","sequence":"additional","affiliation":[{"name":"Department Radiology, Innsbruck Medical University, 6020 Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4456-1822","authenticated-orcid":false,"given":"Fabian","family":"Barbieri","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum Charite, 10117 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7255-0672","authenticated-orcid":false,"given":"Gerlig","family":"Widmann","sequence":"additional","affiliation":[{"name":"Department Radiology, Innsbruck Medical University, 6020 Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6755-4399","authenticated-orcid":false,"given":"Gudrun M.","family":"Feuchtner","sequence":"additional","affiliation":[{"name":"Department Radiology, Innsbruck Medical University, 6020 Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9892152","DOI":"10.34133\/2021\/9892152","article-title":"Recent Development in X-Ray Imaging Technology: Future and Challenges","volume":"2021","author":"Ou","year":"2021","journal-title":"Research"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"633","DOI":"10.5152\/dir.2021.20497","article-title":"Chest X-ray in intensive care unit patients: What there is to know about thoracic devices","volume":"27","author":"Baratella","year":"2021","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1001\/archinternmed.2011.234","article-title":"Effect of computerized physician order entry on radiologic examination order indication quality","volume":"171","author":"Schneider","year":"2011","journal-title":"Arch. Intern. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.acra.2005.10.017","article-title":"Evaluation of the quality of radiology requisitions for intensive care unit patients","volume":"13","author":"Cohen","year":"2006","journal-title":"Acad. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1007\/s11845-023-03377-8","article-title":"GPT-4: A new era of artificial intelligence in medicine","volume":"192","author":"Waisberg","year":"2023","journal-title":"Ir. J. Med. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4096","DOI":"10.1097\/JS9.0000000000001359","article-title":"Step into the era of large multimodal models: A pilot study on ChatGPT-4V(ision)\u2019s ability to interpret radiological images","volume":"110","author":"Zhu","year":"2024","journal-title":"Int. J. Surg."},{"key":"ref_7","first-page":"187","article-title":"A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke","volume":"31","author":"Kuzan","year":"2025","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.21037\/qims-23-892","article-title":"The role of large language models in medical image processing: A narrative review","volume":"14","author":"Tian","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1001\/jamaophthalmol.2024.0017","article-title":"Accuracy of an Artificial Intelligence Chatbot\u2019s Interpretation of Clinical Ophthalmic Images","volume":"142","author":"Mihalache","year":"2024","journal-title":"JAMA Ophthalmol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1097\/RLU.0000000000005207","article-title":"Keeping Up With ChatGPT: Evaluating Its Recognition and Interpretation of Nuclear Medicine Images","volume":"49","author":"Rogasch","year":"2024","journal-title":"Clin. Nucl. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e240153","DOI":"10.1148\/radiol.240153","article-title":"Performance of GPT-4 with Vision on Text- and Image-based ACR Diagnostic Radiology In-Training Examination Questions","volume":"312","author":"Hayden","year":"2024","journal-title":"Radiology"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, C.J., Sobol, K., Hickey, C., and Raphael, J. (Hand, 2024). The Comparative Performance of Large Language Models on the Hand Surgery Self-Assessment Examination, Hand, Epub ahead of print.","DOI":"10.1177\/15589447241279460"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lyu, Q., Tan, J., Zapadka, M.E., Ponnatapura, J., Niu, C., Myers, K.J., Wang, G., and Whitlow, C.T. (2023). Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: Results, limitations, and potential. Vis. Comput. Ind. Biomed. Art., 6.","DOI":"10.1186\/s42492-023-00136-5"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e231040","DOI":"10.1148\/radiol.231040","article-title":"ChatGPT\u2019s Diagnostic Performance from Patient History and Imaging Findings on the Diagnosis Please Quizzes","volume":"308","author":"Ueda","year":"2023","journal-title":"Radiology"},{"key":"ref_15","first-page":"e43958","article-title":"Artificial Intelligence (AI) in Radiology: A Deep Dive Into ChatGPT 4.0\u2019s Accuracy with the American Journal of Neuroradiology\u2019s (AJNR) \u201cCase of the Month\u201d","volume":"15","author":"Suthar","year":"2023","journal-title":"Cureus"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s00234-023-03252-4","article-title":"Accuracy of ChatGPT generated diagnosis from patient\u2019s medical history and imaging findings in neuroradiology cases","volume":"66","author":"Horiuchi","year":"2024","journal-title":"Neuroradiology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1007\/s00270-024-03803-z","article-title":"Using ChatGPT to Improve Readability of Interventional Radiology Procedure Descriptions","volume":"47","author":"Zaki","year":"2024","journal-title":"Cardiovasc. Intervent. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Truhn, D., Weber, C.D., Braun, B.J., Bressem, K., Kather, J.N., Kuhl, C., and Nebelung, S. (2023). A pilot study on the efficacy of GPT-4 in providing orthopedic treatment recommendations from MRI reports. Sci. Rep., 13, Erratum in Sci. Rep. 2024, 14, 5431. https:\/\/doi.org\/10.1038\/s41598-024-56029-x.","DOI":"10.1038\/s41598-023-47500-2"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Horiuchi, D., Tatekawa, H., Oura, T., Shimono, T., Walston, S.L., Takita, H., Matsushita, S., Mitsuyama, Y., Miki, Y., and Ueda, D. (Eur. Radiol., 2024). ChatGPT\u2019s diagnostic performance based on textual vs. visual information compared to radiologists\u2019 diagnostic performance in musculoskeletal radiology, Eur. Radiol., Epub ahead of print.","DOI":"10.1007\/s00330-024-10902-5"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2826","DOI":"10.1007\/s00330-023-10230-0","article-title":"Evaluating the reliability of ChatGPT as a tool for imaging test referral: A comparative study with a clinical decision support system","volume":"34","author":"Rosen","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1016\/j.jacr.2023.06.009","article-title":"ChatGPT-4 Assistance in Optimizing Emergency Department Radiology Referrals and Imaging Selection","volume":"20","author":"Barash","year":"2023","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_22","first-page":"604","article-title":"Large language models can help with biostatistics and coding needed in radiology research","volume":"14","author":"Ghosh","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e232756","DOI":"10.1148\/radiol.232756","article-title":"Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications","volume":"310","author":"Bhayana","year":"2024","journal-title":"Radiology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1007\/s11604-024-01606-3","article-title":"Evaluating ChatGPT-4V in chest CT diagnostics: A critical image interpretation assessment","volume":"42","author":"Dehdab","year":"2024","journal-title":"Jpn. J. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1007\/s00330-024-11032-8","article-title":"Comparative analysis of GPT-4-based ChatGPT\u2019s diagnostic performance with radiologists using real-world radiology reports of brain tumors","volume":"35","author":"Mitsuyama","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_27","first-page":"e68298","article-title":"GPT-4 Vision: Multi-Modal Evolution of ChatGPT and Potential Role in Radiology","volume":"16","author":"Javan","year":"2024","journal-title":"Cureus"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.clinimag.2023.06.008","article-title":"Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports","volume":"101","author":"Li","year":"2023","journal-title":"Clin. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, Y., Gomaa, A., Semrau, S., Haderlein, M., Lettmaier, S., Weissmann, T., Grigo, J., Ben Tkhayat, H., Frey, B., and Gaipl, U. (2023). Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: Potentials and challenges for ai-assisted medical education and decision making in radiation oncology. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1265024"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1016\/j.jacr.2023.07.010","article-title":"Using Artificial Intelligence Chatbots as a Radiologic Decision-Making Tool for Liver Imaging: Do ChatGPT and Bard Communicate Information Consistent With the ACR Appropriateness Criteria?","volume":"20","author":"Patil","year":"2023","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Derevianko, A., Pizzoli, S.F.M., Pesapane, F., Rotili, A., Monzani, D., Grasso, R., Cassano, E., and Pravettoni, G. (2023). The Use of Artificial Intelligence (AI) in the Radiology Field: What Is the State of Doctor-Patient Communication in Cancer Diagnosis?. Cancers, 15.","DOI":"10.3390\/cancers15020470"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1148\/radiol.2019182465","article-title":"Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs","volume":"294","author":"Sim","year":"2020","journal-title":"Radiology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s41747-020-00203-z","article-title":"Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: A first experience from Lombardy, Italy","volume":"5","author":"Castiglioni","year":"2021","journal-title":"Eur. Radiol. Exp."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zandehshahvar, M., van Assen, M., Maleki, H., Kiarashi, Y., De Cecco, C.N., and Adibi, A. (2021). Toward understanding COVID-19 pneumonia: A deep-learning-based approach for severity analysis and monitoring the disease. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-90411-3"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Akter, S., Shamrat, F.M.J.M., Chakraborty, S., Karim, A., and Azam, S. (2021). COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images. Biology, 10.","DOI":"10.3390\/biology10111174"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/TMI.2020.2993291","article-title":"Deep learning COVID-19 features on CXR using limited training data sets","volume":"39","author":"Oh","year":"2020","journal-title":"IEEE Trans. Med. Imaging."},{"key":"ref_37","first-page":"87","article-title":"Deep transfer learning based classification model for COVID-19 disease","volume":"43","author":"Pathak","year":"2022","journal-title":"Ing. Rech. Biomed."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101794","DOI":"10.1016\/j.media.2020.101794","article-title":"Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning","volume":"65","author":"Minaee","year":"2020","journal-title":"Med. Image. Anal."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"409","DOI":"10.7326\/M23-1898","article-title":"Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs: A Risk Prediction Study","volume":"177","author":"Weiss","year":"2024","journal-title":"Ann. Intern. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s11604-024-01552-0","article-title":"The impact of large language models on radiology: A guide for radiologists on the latest innovations in AI","volume":"42","author":"Nakaura","year":"2024","journal-title":"Jpn. J. Radiol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shieh, A., Tran, B., He, G., Kumar, M., Freed, J.A., and Majety, P. (2024). Assessing ChatGPT 4.0\u2019s test performance and clinical diagnostic accuracy on USMLE STEP 2 CK and clinical case reports. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-58760-x"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e2440969","DOI":"10.1001\/jamanetworkopen.2024.40969","article-title":"Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial","volume":"7","author":"Goh","year":"2024","journal-title":"JAMA Netw. Open."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kochanek, M., Cichecki, I., Kaszyca, O., Szyd\u0142o, D., Madej, M., J\u0119drzejewski, D., Kazienko, P., and Koco\u0144, J. (2024). Improving Training Dataset Balance with ChatGPT Prompt Engineering. Electronics, 13.","DOI":"10.3390\/electronics13122255"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e0805","DOI":"10.1097\/RTI.0000000000000805","article-title":"The Diagnostic Performance of Large Language Models and General Radiologists in Thoracic Radiology Cases: A Comparative Study","volume":"40","author":"Gunes","year":"2025","journal-title":"J. Thorac. Imaging."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Arnold, P.G., Russe, M.F., Bamberg, F., Emrich, T., Vecsey-Nagy, M., Ashi, A., Kravchenko, D., Varga-Szemes, \u00c1., Soschynski, M., and Rau, A. (J. Cardiovasc. Comput. Tomogr., 2025). Performance of large language models for CAD-RADS 2.0 classification derived from cardiac CT reports, J. Cardiovasc. Comput. Tomogr., Epub ahead of print.","DOI":"10.1016\/j.jcct.2025.03.007"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1145\/3705725","article-title":"Recent Advances of Foundation Language Models-based Continual Learning: A Survey","volume":"57","author":"Yang","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_47","first-page":"e59857","article-title":"Accuracy and Repeatability of ChatGPT Based on a Set of Multiple-Choice Questions on Objective Tests of Hearing","volume":"16","author":"Kochanek","year":"2024","journal-title":"Cureus"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, Q., Sun, H., Liu, H., Jiang, Y., Ran, T., Jin, X., Xiao, X., Lin, Z., Chen, H., and Niu, Z. (2023). An extensive benchmark study on biomedical text generation and mining with ChatGPT. Bioinformatics, 39.","DOI":"10.1093\/bioinformatics\/btad557"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1038\/s41591-021-01517-0","article-title":"A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI","volume":"27","author":"Sounderajah","year":"2021","journal-title":"Nat. Med."},{"key":"ref_50","first-page":"e220159","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update","volume":"6","author":"Mongan","year":"2024","journal-title":"Radiol. Artif. Intell."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/15\/5\/194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:30:29Z","timestamp":1760031029000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/15\/5\/194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,10]]},"references-count":50,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["jpm15050194"],"URL":"https:\/\/doi.org\/10.3390\/jpm15050194","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,10]]}}}