MIRROR: Iterative Neuro-Symbolic Reasoning for Human-Like Clinical Decision Making

Effective clinical decision-making often unfolds as an iterative loop: attention is first allocated to a subset of patient symptoms and findings, symbolic medical reasoning is applied, and the results of reasoning guide subsequent clinical inquiries and inference. We present MIRROR (Multi-Iterative Reasoning with Rules and Observation Refinement), a trainable neuro-symbolic architecture that explicitly models this loopy perception–reasoning dynamic in medical diagnosis. MIRROR alternates between (i) an adaptive perception module that selectively attends to clinical features and patient state based on evolving diagnostic hypotheses, and (ii) a differentiable reasoning module that evaluates symbolic medical rules and clinical predicates in a learned embedding space. Across multiple cycles, predicate-level feedback from reasoning drives refined attention to further patient evidence, yielding an inspectable chain of diagnostic inference. Controlled experiments on three diagnostic benchmarks quantify diagnostic accuracy, path recovery, and evidence acquisition, while cycle-by-cycle traces expose when observation, implication, diagnosis, and knowledge-region routing change. MIRROR therefore provides an explicit computational model of iterative clinical decision making.