TRACE: Towards Reliable, Auditable Clinical Evidence through Tool-Augmented Reasoning for Cine CMR Diagnosis

Cine cardiac magnetic resonance (CMR) assesses cardiac structure and function. Despite advances in vision-language models (VLMs), automated diagnosis requires verifiable quantitative evidence and explicit checks of diagnostic sufficiency to avoid premature conclusions. We introduce TRACE (Towards Reliable, Auditable Clinical Evidence), a training-free agentic framework integrating adaptive tool use, clinical guideline retrieval, and independent evidence verification. Guided by clinical knowledge, the agent acquires CMR measurements, updates diagnostic hypotheses, and selects the next measurement. An independent verifier checks diagnostic requirements and returns evidence gaps for targeted acquisition and reassessment. Unresolved gaps lead to deferral for clinician review; accepted decisions produce structured reports linking findings, diagnostic rationale, clinical sources, and follow-up recommendations. On a development cohort of 50 balanced ACDC cases, TRACE achieves 87.3% mean accuracy across eight seeds with 96.3% diagnosis coverage, compared with 85.3% for ACDC-finetuned CineMA and 16%–28% for eight single-pass VLMs. Its report compiler achieves 100% specification conformance versus 46% for an LLM given the same patient evidence. These results demonstrate the value of evidence-guided agentic reasoning for auditable cine CMR diagnosis and reporting without additional model training.