ChatGPT-4-Based Automated Preliminary Clinical Reporting in Myocardial Perfusion Imaging: A Pilot Evaluation

Abstract

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—clinical accuracy, report structure, terminological appropriateness, and overall comprehensibility—scored on a 5-point Likert scale. Inter-observer agreement was assessed using Cohen’s 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’s κ = 0.78; 95% CI: 0.64–0.92). 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.