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.

Integrating multimodal clinical records—such as Electronic Health Records (EHR) and free-text clinical reports—has shown great potential in predicting clinical outcomes. However, prior work has primarily focused on capturing temporal interactions within individual samples and fusing multimodal information, overlooking critical temporal patterns across different patients. These patterns, such as trends in vital signs like abnormal heart rate or blood pressure, can indicate deteriorating health or an impending critical event of any individual in a given population. Similarly, clinical notes often contain textual descriptions that reflect these changes. Identifying corresponding temporal patterns across different modalities is crucial for improving the accuracy of clinical outcome predictions, yet it remains a challenging task. To address this gap, we introduce a Cross-modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data. Our approach introduces shared initial temporal pattern representations and refines them using slot attention to generate temporal semantic embeddings. To ensure rich cross-modal temporal semantics in the learned patterns, we introduce a Temporal Pattern Noise Contrastive Estimation (TP-NCE) loss for cross-modal alignment, along with two reconstruction losses to retain core information of each modality. Evaluations on two clinically critical tasks—48 hour in-hospital mortality and 24-hour phenotype classification—using the MIMIC-III database demonstrate the superiority of our method over existing approaches. The code is anonymously available at https://github.com/HKU-MedAI/CTPD.