Multidisciplinary attitudes towards LLM-structured oncologic CT reports: a prospective fixed-sequence questionnaire study.

OBJECTIVES: Large language models (LLMs) can automate the conversion of radiologic free text into structured reports, but multidisciplinary reader preferences for the resulting output remain insufficiently studied. This prospective, two-phase, fixed-sequence within-participant study evaluated reader assessments of LLM-structured versus conventional free-text oncologic CT reports. MATERIAL & METHODS: A paired convenience sample of radiologists, radiology technologists, oncologists, and medical students was recruited between May and November 2025. In Phase 1, participants rated 5 free-text oncologic CT reports for readability and comprehension (3 items), oncologic information quality (5 items), and clinical utility (4 items) on a 9-point Likert scale. After a washout of at least 4 weeks, participants rated the LLM-structured version generated by Llama-4-Scout-17B-16E-Instruct. RESULTS: Sixty-six paired participants (24 medical students, 22 radiologists, 12 radiology technologists, 8 oncologists) were included (mean [standard deviation] age, 30.5 [7.6] years; 37 [56.1%] female). Structured reports showed significantly higher odds of agreement for 11 of 12 items (odds ratio [OR] range, 1.70-4.81), with strongest effects for clinical utility (OR, 4.81; 95% CI, 3.24-7.15), information findability (OR, 4.31; 95% CI, 3.12-5.96), recommendation clarity (OR, 4.14; 95% CI, 2.95-5.83), and patient communication suitability (OR, 3.45; 95% CI, 2.59-4.60). Radiology technologists showed significantly higher ratings for all 12 items, radiologists for 6, students for 7, and oncologists for none. CONCLUSION: In this single-center paired sample, LLM-structured oncologic CT reports received higher reader ratings than conventional free-text reports across most evaluated dimensions, with the largest differences observed for information findability and clinical utility. These findings support further evaluation of radiologist-supervised LLM-assisted report structuring, including formal source-to-output validation and randomized multicenter assessment, before clinical implementation.