ChatGPT-4-Based Automated Preliminary Clinical Reporting in Myocardial Perfusion Imaging: A Pilot Evaluation.
- 1Department of Nuclear Medicine, Mersin City Training and Research Hospital, 33240 Mersin, Turkey.
- 2Department of Family Medicine, Mersin City Training and Research Hospital, 33240 Mersin, Turkey.
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.
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BibTeX
@article{keskin2026chatgpt4based,
title = {{ChatGPT-4-Based Automated Preliminary Clinical Reporting in Myocardial Perfusion Imaging: A Pilot Evaluation.}},
author = {Mutlay Keskin and Ece Oğuz},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = sep,
doi = {10.3390/diagnostics16193110},
url = {https://europepmc.org/article/MED/42882264},
note = {CC BY},
}
RIS
TY - JOUR
TI - ChatGPT-4-Based Automated Preliminary Clinical Reporting in Myocardial Perfusion Imaging: A Pilot Evaluation.
AU - Mutlay Keskin
AU - Ece Oğuz
PY - 2026
DA - 2026/09/24
JO - Diagnostics (Basel, Switzerland)
DO - 10.3390/diagnostics16193110
UR - https://europepmc.org/article/MED/42882264
AB - 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.
SN - 2075-4418
ER -