RADAR: A Prototype for Grounding Large Language Model Consult Support in an ACR-Structured Appropriateness Knowledge...

Large language models (LLMs) answer medical questions fluently but fabricate statements and citations too often to be trusted unaided at the point of care. We describe RADAR (Radiology Appropriateness Decision and Advisory Resource), a prototype retrieval-augmented consult-support pipeline that grounds an LLM in a knowledge base structured like the American College of Radiology (ACR) Appropriateness Criteria at the level of individual clinical variants. The knowledge base is synthetic (twelve variants across five topics) because the official criteria are copyrighted. The design is deliberately conservative: a BM25 retriever, an absolute-score abstention gate, a structured-output generator bound by an in-prompt grounding contract, and a deterministic validator that strips any recommendation whose citation does not resolve to a retrieved chunk. We report a development set (eight scenarios, seven off-domain probes) used during parameter selection and a held-out set written after all parameters were frozen (36 scenarios, three per variant, and 24 clinically adjacent probes), evaluated in a deterministic retrieval-only mode and in the full pipeline over five independent runs at temperature 0. Retrieval alone ranked the correct variant first in 25 of 36 held-out scenarios (69.4%; 95% CI 51.9-83.7) but always placed it within the top five, and the score gate stopped only 3 of 22 clinically adjacent probes. The full pipeline (claude-sonnet-4-6, temperature 0, five runs) selected the correct variant and top procedure in every run for 33 of 36 scenarios (91.7%; 95% CI 77.5-98.2) and in 33 or 34 of 36 scenarios in each individual run; every failed run was a false abstention on a thunderclap-headache presentation, and no run of any scenario recommended a wrong variant. It refused 17 of 22 clinically adjacent probes (77.3%; 54.6-92.2). All 721 emitted citations resolved to a retrieved chunk. The findings characterize a proof of concept: lexical retrieval delivers topic routing and citation traceability, while variant selection and realistic abstention depend on the generator acting as a semantic reranker, and the residual failures are refusals rather than wrong answers. Clinical readiness will require licensed ACR content, a larger reference-standard set, and prospective evaluation.