{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:05:00Z","timestamp":1790697900573,"version":"4.1.0"},"reference-count":19,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"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\/Objectives: Myocardial perfusion scintigraphy (MPS) is a cornerstone non-invasive imaging modality for assessing myocardial ischemia and infarction. This pilot study aimed to evaluate the feasibility of using ChatGPT-4 for automated preliminary clinical reporting in MPS and to identify specific scenarios in which large language model (LLM) performance is robust versus inadequate. Methods: A comparative analysis was conducted using 30 consecutive de-identified MPS cases spanning a broad spectrum of clinical scenarios. Structured clinical data were input into ChatGPT-4 to generate AI-based preliminary reports, which were then compared with reports prepared by two experienced nuclear medicine physicians. Reports were independently evaluated using four criteria\u2014clinical accuracy, report structure, terminological appropriateness, and overall comprehensibility\u2014scored on a 5-point Likert scale. Inter-observer agreement was assessed using Cohen\u2019s kappa. Results: ChatGPT-4 demonstrated strong performance in report structure (median 5), terminological appropriateness (median 4), and overall comprehensibility (median 5), consistently producing well-organized and coherent reports. However, clinical accuracy was significantly lower compared with physician reports (median 4 vs. 5; p = 0.002; effect size r = 0.52), particularly in complex cases such as multivessel ischemia and mixed pathologies, where outputs were occasionally superficial or lacked specificity. Inter-observer agreement between evaluating physicians was substantial (Cohen\u2019s \u03ba = 0.78; 95% CI: 0.64\u20130.92). Conclusions: ChatGPT-4 shows promise as a supportive tool for preliminary MPS reporting and medical education, but its limitations in higher-order clinical reasoning necessitate careful human oversight. These findings are preliminary and require validation in adequately powered, multicenter studies before any clinical implementation.<\/jats:p>","DOI":"10.3390\/diagnostics16193110","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:29:53Z","timestamp":1790267393000},"page":"3110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ChatGPT-4-Based Automated Preliminary Clinical Reporting in Myocardial Perfusion Imaging: A Pilot Evaluation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2528-8648","authenticated-orcid":false,"given":"Mutlay","family":"Keskin","sequence":"first","affiliation":[{"name":"Department of Nuclear Medicine, Mersin City Training and Research Hospital, 33240 Mersin, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ece","family":"O\u011fuz","sequence":"additional","affiliation":[{"name":"Department of Family Medicine, Mersin City Training and Research Hospital, 33240 Mersin, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S229","DOI":"10.1259\/bjr\/14625142","article-title":"Myocardial perfusion scintigraphy: Past, present and future","volume":"84","author":"Notghi","year":"2011","journal-title":"Br. J. Radiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1093\/ehjci\/jew038","article-title":"Multicentre multi-device hybrid imaging study of coronary artery disease: Results from the EValuation of INtegrated Cardiac Imaging for the Detection and Characterization of Ischaemic Heart Disease (EVINCI) hybrid imaging population","volume":"17","author":"Liga","year":"2016","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1053\/j.semnuclmed.2025.01.006","article-title":"The evolution of artificial intelligence in nuclear medicine","volume":"55","author":"Lopes","year":"2025","journal-title":"Semin. Nucl. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e48933","DOI":"10.2196\/48933","article-title":"Applications of the natural language processing tool ChatGPT in clinical practice: Comparative study and augmented systematic review","volume":"11","author":"Schopow","year":"2023","journal-title":"JMIR Med. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10916-024-02075-x","article-title":"ChatGPT: A conceptual review of applications and utility in the field of medicine","volume":"48","author":"Rao","year":"2024","journal-title":"J. Med. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1007\/s11606-024-08657-2","article-title":"Large language models in health care: Charting a path toward accurate, explainable, and secure AI","volume":"39","author":"Khullar","year":"2024","journal-title":"J. Gen. Intern. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1007\/s00330-024-11107-6","article-title":"Large language models for structured reporting in radiology: Past, present, and future","volume":"35","author":"Busch","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e230725","DOI":"10.1148\/radiol.230725","article-title":"Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: A multilingual feasibility study","volume":"307","author":"Adams","year":"2023","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e223312","DOI":"10.1148\/radiol.223312","article-title":"ChatGPT and the future of medical writing","volume":"307","author":"Biswas","year":"2023","journal-title":"Radiology"},{"key":"ref_10","unstructured":"Klang, E., Collins, J.D., Glicksberg, B.S., Korfiatis, P., Nadkarni, G., and Sorin, V. (2025). Large language models in radiology reporting: A systematic review of performance, limitations, and clinical implications. medRxiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2452","DOI":"10.1007\/s00259-025-07101-9","article-title":"Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: A pilot single center study","volume":"52","author":"Choi","year":"2025","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1007\/s12149-024-01981-x","article-title":"Generative AI and large language models in nuclear medicine: Current status and future prospects","volume":"38","author":"Hirata","year":"2024","journal-title":"Ann. Nucl. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s41747-025-00671-1","article-title":"Reliability of Gemini 2.5 Pro, ChatGPT 4.1, DeepSeek V3, and Claude Opus 4 in generating standardized CMR protocols","volume":"10","author":"Licu","year":"2026","journal-title":"Eur. Radiol. Exp."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2817","DOI":"10.1007\/s00330-023-10213-1","article-title":"ChatGPT makes medicine easy to swallow: An exploratory case study on simplified radiology reports","volume":"34","author":"Jeblick","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e51346","DOI":"10.2196\/51346","article-title":"ChatGPT as a tool for medical education and clinical decision-making on the wards: Case study","volume":"8","author":"Skryd","year":"2024","journal-title":"JMIR Form. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e48808","DOI":"10.2196\/48808","article-title":"ChatGPT-generated differential diagnosis lists for complex case-derived clinical vignettes: Diagnostic accuracy evaluation","volume":"11","author":"Hirosawa","year":"2023","journal-title":"JMIR Med. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e51151","DOI":"10.2196\/51151","article-title":"Incorporating ChatGPT in medical informatics education: Mixed methods study on student perceptions and experiential integration proposals","volume":"10","year":"2024","journal-title":"JMIR Med. Educ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e48009","DOI":"10.2196\/48009","article-title":"Ethical considerations of using ChatGPT in health care","volume":"25","author":"Wang","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Zhang, Z.M. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02103-9"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:23:00Z","timestamp":1790695380000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":19,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["diagnostics16193110"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16193110","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}