Multidisciplinary attitudes towards LLM-structured oncologic CT reports: a prospective fixed-sequence questionnaire study.
13 affiliations
- 1Institute for Diagnostic and Interventional Radiology, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany. [email protected].
- 2Institute of Artificial Intelligence in Medicine and Healthcare, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Munich, Germany. [email protected].
- 3Institute for Diagnostic and Interventional Radiology, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.
- 4Institute of Diagnostic and Interventional Neuroradiology, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.
- 5Department of Radiology, University Medical Center Regensburg, Regensburg, Germany.
- 6Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, England, UK.
- 7Institute of Artificial Intelligence in Medicine and Healthcare, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Munich, Germany.
- 8Department of Gynecology and Center for Hereditary Breast and Ovarian Cancer, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.
- 9Center for Personalized Medicine (ZPM), TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.
- 10Department of Radiation Oncology, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.
- 11Bavarian Center for Cancer Research (BZKF), Technical University Munich, Munich, Germany.
- 12Institute of Radiation Medicine, Helmholtz Zentrum München, Munich, Germany.
- 13German Cancer Consortium, Partner Site Munich, Munich, Germany.
Abstract
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.
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BibTeX
@article{busch2026multidisciplinary,
title = {{Multidisciplinary attitudes towards LLM-structured oncologic CT reports: a prospective fixed-sequence questionnaire study.}},
author = {Felix Busch and Alexander W Marka and Lilly F Wingberg and Philipp Prucker and Su Hwan Kim and Quirin Strotzer and Christina Schmidt and Jacqueline Lammert and Kai J Borm and Jan Peeken and Marcus R Makowski and Lisa C Adams and Keno K Bressem},
journal = {Cancer imaging : the official publication of the International Cancer Imaging Society},
year = {2026},
month = oct,
doi = {10.1186/s40644-026-01137-2},
url = {https://europepmc.org/article/MED/42850632},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multidisciplinary attitudes towards LLM-structured oncologic CT reports: a prospective fixed-sequence questionnaire study.
AU - Felix Busch
AU - Alexander W Marka
AU - Lilly F Wingberg
AU - Philipp Prucker
AU - Su Hwan Kim
AU - Quirin Strotzer
AU - Christina Schmidt
AU - Jacqueline Lammert
AU - Kai J Borm
AU - Jan Peeken
AU - Marcus R Makowski
AU - Lisa C Adams
AU - Keno K Bressem
PY - 2026
DA - 2026/10/08
JO - Cancer imaging : the official publication of the International Cancer Imaging Society
DO - 10.1186/s40644-026-01137-2
UR - https://europepmc.org/article/MED/42850632
AB - 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.
SN - 1740-5025
SN - 1470-7330
ER -