Large language models (LLMs) have shown strong promise as clinical reasoning agents, yet most benchmarks assume complete evidence availability before reasoning begins. However, real clinical practice unfolds through dynamic clinical workflows with progressive disclosure of patient evidence, requiring clinicians to decide what evidence to acquire next from partial and evolving patient states. This mismatch exposes a bottleneck in proactive evidence acquisition for LLM-based clinical agents. To address this gap, we introduce **ProActIS**, a lightweight state-conditioned examination proposer for proactive evidence acquisition. Given an evolving patient state, ProActIS ranks standardized laboratory, microbiology, and radiology examinations, together with STOP, through structured patient-state decomposition and action-card encoding. We construct an MIMIC-IV benchmark with 81,960 hospital admissions and 860,099 turn-level patient-state/examination pairs, using explicit diagnosis/result filtering and retrospective leakage sensitivity analysis over 129 standardized examination actions plus STOP (130 actions). On full-bank next-examination ranking, ProActIS achieves 0.6028 Recall@1, 0.8868 Recall@5, and 0.7258 MRR, substantially outperforming frequency and LLM baselines. In LLM-assisted dynamic workflows, ProActIS substantially improves benchmark-level evidence coverage and documented-trajectory alignment for downstream agents, with downstream diagnostic accuracy approaching the corresponding all-information controls.

Dental triage is a safety-critical clinical routing task that requires integrating multimodal clinical information (e.g., patient complaints and radiographic evidence) to determine complete referral plans. We present Dental-TriageBench, the first expert-annotated benchmark for reasoning-driven multimodal dental triage. Built from authentic outpatient workflows, it contains 246 de-identified cases annotated with expert-authored golden reasoning trajectories, together with hierarchical triage labels. We benchmark 19 proprietary, open-source, and medical-domain MLLMs against three junior dentists serving as the human baseline, and find a substantial human–model gap, on fine-grained treatment-level triage. Further analyses show that accurate triage requires both complaint and OPG information, and that model errors concentrate on cases with multiple referral domains, where MLLMs tend to produce overly narrow referral sets and omission-heavy errors. Dental-TriageBench provides a realistic testbed for developing multimodal clinical AI systems that are more clinically grounded, coverage-aware, and safer for downstream care.