Abstract
Abstract
Background
Many radiology decisions, such as whether a report describes a finding or how urgently a study should be read, are choices among a fixed set of answers. Decision models return a probability for each permitted answer in one step, without generating text, and an open-weight model can run within a hospital so that patient data stay local. No decision model has yet been specialized for radiology and compared with large language models (LLMs).
Methods
We fine-tuned the general-purpose decision models Kev-27B (Qwen3.8-27B backbone) and Kev-9B on 36,109 records from public radiology datasets to obtain RadKev-27B and RadKev-9B, and compared them with the Kev models, the hosted decision models Jev and OpenAI Decisions, and the LLM Qwen3.8-27B, on 14,142 held-out questions in fifteen tasks (report reading, case diagnosis and classification, radiological knowledge). The prespecified primary outcome was task-averaged accuracy relative to Kev-27B.
Results
RadKev-27B exceeded Kev-27B by 4.9 percentage points (95% CI 3.8 to 6.2), with the largest gain in report error detection (22.6 points); RadKev-9B exceeded Kev-9B by 6.9 points (5.5 to 8.4) and did not differ detectably from Kev-27B, a model three times its size. RadKev-27B was more accurate than the hosted decision models Jev (3.0 points, 1.7 to 4.4) and OpenAI Decisions (4.8, 3.2 to 6.4), than Qwen3.8-27B without reasoning (4.7, 3.4 to 6.0) and with reasoning (2.8 points, 1.0 to 4.6) on 1,675 questions, at 132-fold lower latency (128 ms vs 16.9 s per question). At an error rate of 5%, RadKev-27B could answer 90.5% of questions and Kev-27B 76.4%.
Conclusions
RadKev-27B was more accurate than two hosted decision models and an LLM of the same size, with or without reasoning, at a fraction of the latency of reasoning, and can be deployed within an institution for categorical radiology decisions after validation on local data.