ArticleJMIR AI2026
Exploring the Role of AI in Enhancing Nuclear Medicine Report Impressions Generated by Trainees and ChatGPT-4o: Comparative Evaluation Study.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Accurate reporting in nuclear medicine is essential for clinical decision-making. Trainees often generate preliminary reports with variable quality, and artificial intelligence (AI) tools such as ChatGPT-4o may enhance report clarity and accuracy, particularly in the impression section of the report. Objective: This study aimed to evaluate and compare the quality of positron emission tomography / computed tomography report impression sections generated by trainees and by the AI chatbot ChatGPT-4o (OpenAI), focusing on correctness, clarity, completeness, organization, use of diagnostic certainty terminology, and physician satisfaction. Methods: The impression sections of 200 positron emission tomography/ computed tomography reports generated by trainees and AI (100 reports each) were compared. The AI generated the impressions based on the stem, clinical history, and technical sections of the trainee-generated reports. Reports were blindly rated by 3 nuclear medicine physicians. Survey questions, including Likert-scale questions, assessed correctness, use of certainty terms, clarity, completeness, organization, and satisfaction. Statistical analyses included Fisher exact test, 2-tailed t tests, chi-square tests, ANOVA, and effect sizes (Cohen d). Thematic analysis was performed on free-text comments. Results: AI-generated impressions were rated as correct in 92% of reports (mean 0.92, SD 0.27) and trainee-generated impressions rated as correct in 91% of reports (mean 0.91, SD 0.29; P=.80, negligible effect size). AI-generated impressions included certainty terms more frequently than trainee-generated impressions (94% vs 81%, respectively; P=.005; χ²2=6.58, small effect size). AI also used higher-level certainty terms more frequently than trainees (mean 4.23, SD 1.25 vs mean 3.69, SD 1.93 on a 5-point scale; P=.02). Clarity was high in both groups (AI: mean 4.49, SD 0.70, and 89.8% of reports rated as clear vs trainees: mean 4.35, SD 0.85, and 87.0% of reports rated as clear; P=.20). Completeness was significantly higher for AI-generated impressions than trainee-generated impressions (mean 4.52, SD 0.76, and 90.4% complete vs mean 4.09, SD 1.01, and 81.8% complete, respectively; P<.001, small-to-medium effect size). Organization was similar between the groups (mean 4.22, SD 0.91 vs mean 4.39, SD 0.74; P=.15). Satisfaction ratings were also compared, with AI-generated impressions achieving a mean score of 4.31 (SD 0.77) and 86.2% satisfaction, compared with a mean score of 4.14 (SD 0.91) and 82.8% satisfaction for trainee-generated impressions (P=.16). Thematic analysis showed that trainee-generated impressions were more frequently criticized for accuracy (74% vs 19%; P<.001) and actionability (20% vs 4.8%; P=.02), whereas AI-generated impressions were more frequently criticized for excessive length (33% vs 0%; P<.001). Conclusions: These findings suggest that the AI chatbot ChatGPT-4o can serve as a valuable adjunct in nuclear medicine reporting, particularly at the resident level, by improving decisiveness and completeness while complementing trainee education and clinical oversight.
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