Large Language Models in Radiology Reporting—A Systematic Review of Performance, Limitations, and Clinical Implications
Year: 2025
Type: preprint
Study design: Systematic Review
Abstract: Background Large language models (LLMs) have emerged as potential tools for automated radiology reporting. However, concerns regarding their fidelity, reliability, and clinical applicability remain. This systematic review examines the current literature on LLM-generated radiology reports. Methods We conducted a systematic search of MEDLINE, Google Scholar, Scopus, and Web of Science to identify studies published between January 2015 and February 2025. Studies evaluating LLM-generated radiology reports were included. The study follows PRISMA guidelines. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results Nine studies met the inclusion criteria. Of these, six evaluated full radiology reports, while three focused on impression generation. Six studies assessed base LLMs, and three evaluated fine-tuned models. Fine-tuned models demonstrated better alignment with expert evaluations and achieved higher performance on natural language processing metrics compared to base models. All LLMs showed hallucinations, misdiagnoses, and inconsistencies. Conclusion LLMs show promise in radiology reporting. However, limitations in diagnostic accuracy and hallucinations necessitate human oversight. Future research should focus on improving evaluation frameworks, incorporating diverse datasets, and prospectively validating AI-generated reports in clinical workflows.
Source: medRxiv
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4408648898