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ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning

Published 19 May 2026 in cs.CL | (2605.20176v1)

Abstract: LLMs and agentic systems have shown promise for clinical decision support, but existing works largely assume that evidence has already been curated and handed to the model. Real-world clinical workflows instead require agents to actively seek, iteratively plan, and synthesize multimodal evidence from heterogeneous sources. In this paper, we introduce ClinSeekAgent, an automated agentic framework for dynamic multimodal evidence seeking that shifts the paradigm from passive evidence consumption to active evidence acquisition. Given only a clinical query and access to raw data sources, ClinSeekAgent gathers evidence by querying medical knowledge bases, navigating raw EHRs, and invoking medical imaging tools; refines its hypotheses as new information emerges; and integrates the collected evidence into grounded clinical decisions. ClinSeekAgent serves both as an inference-time agent for frontier LLMs and as a training-time pipeline for distilling high-quality agent trajectories into compact open-source models. To validate its inference-time effectiveness, we construct ClinSeek-Bench, which pairs Curated Input reasoning from fixed pre-selected evidence with Automated Evidence-Seeking over raw clinical data. On text-only EHR tasks, ClinSeekAgent improves Claude Opus 4.6 from 60.0 to 63.2 overall F1 and MiniMax M2.5 from 43.1 to 47.3, with positive risk-prediction gains in 7 out of 9 evaluated host models. On multimodal tasks, ClinSeekAgent improves Claude Opus 4.6 from 47.5 to 62.6 (+15.1); all evaluated models improve across the three CXR-related task groups. We further validate ClinSeekAgent as a training pipeline by distilling agentic evidence-seeking trajectories into ClinSeek-35B-A3B, which achieves 34.0 average F1 on existing AgentEHR-Bench, improving over its Qwen3.5-35B-A3B baseline by +11.9 points and approaching Claude Opus 4.6.

Summary

  • The paper demonstrates that ClinSeekAgent improves risk prediction F1 scores across EHR and imaging benchmarks.
  • It integrates 20 specialized tools for real-time, multimodal evidence acquisition from diverse clinical data sources.
  • The framework’s distillation process transfers agentic behavior to compact models, nearly matching advanced LLM performance.

ClinSeekAgent: Automated Multimodal Evidence Seeking for Agentic Clinical Reasoning

Introduction

The paper "ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning" (2605.20176) presents a unified agentic framework targeting the automation of evidence acquisition in clinical decision support. Modern LLM and agentic systems, although effective when provided with curated evidence, do not reflect actual clinical workflows in which evidence must be actively sought from heterogeneous, multimodal sources including raw EHRs, medical knowledge bases, and imaging modalities. ClinSeekAgent reformulates the paradigm, enabling agentic models to iteratively interact with diverse clinical tools and dynamically synthesize multimodal evidence in real-time.

Framework Architecture and Tooling

ClinSeekAgent is structured around three primary evidence sources: longitudinal EHR retrieval, browser-based external medical knowledge search, and medical imaging (DICOM/CXR analysis). It defines 20 specialized tools spanning schema inspection, SQL querying, temporal aggregation, keyword/semantic candidate retrieval, browser querying, and multimodal imaging pipelines. The interaction protocol is formalized such that, given a clinical query, the agent operates with only raw data access, invoking tools and refining its hypothesis trajectory as new information is acquired.

Evidence-seeking is encoded not as a fixed sequence but as an adaptive trajectory, with tool choice and order determined by model policy—supporting compositional and interleaved use across modalities. The pipeline standardizes the raw data environment and tool interface, but does not dictate retrieval order, allowing flexible evidence-seeking policies induced by the agentic models.

Evaluation: Inference-time Paradigm Shift

To rigorously validate ClinSeekAgent's efficacy, the authors construct ClinSeek-Bench, which pairs each clinical task into two settings: (1) Curated Input, where inference is performed on pre-selected evidence packages, and (2) Automated Evidence-Seeking, requiring agentic models to recover evidence from raw data sources using ClinSeekAgent tools.

Numerical Results

  • Text-only EHR Tasks: Strong agentic models show significant gains with ClinSeekAgent. Claude Opus 4.6 improves overall F1 from 60.0 to 63.2, and MiniMax M2.5 from 43.1 to 47.3. Gains concentrate in risk prediction (Mortality Hospital: +12.5, LengthOfStay: +16.2, ED Hospitalization: +12.5).
  • Multimodal Tasks: Gains are more pronounced. Claude Opus 4.6 jumps from 47.5 to 62.6 F1 (+15.1), with consistent improvements across CXR finding, enumeration, and change comparison tasks. Notably, phenotype prediction sees a +34.0 point gain.
  • Model Dependence: Gains are robust for LLMs with advanced planning and tool-use skills, but weaker or less multimodal-capable models achieve less stable improvements or may underperform the Curated Input baseline.

Analysis

ClinSeekAgent's advantage is concentrated in settings where evidence is longitudinal, sparse, or distributed—especially in risk prediction or multimodal benchmarks with complex cross-modal dependencies. For decision-making subtasks, improvements are less pronounced and sometimes negative, attributed to cases where the agent fails to isolate critical signals amidst voluminous data. The paradigm shift supports active evidence acquisition, directly mirroring realistic clinical workflows, and exposes patient-specific, multimodal signals missed in fixed curated settings.

Training-time Distillation: Transferring Agentic Behavior

ClinSeekAgent also serves as a pipeline for generating supervision and distilling the evidence-seeking process into compact model architectures. Using trajectories generated by Claude Opus 4.6, the authors fine-tune Qwen3.5-35B-A3B into ClinSeek-35B-A3B. On AgentEHR-Bench, the distilled model achieves 34.0 average F1 (+11.9 over baseline), closing 94.4% of the gap to Claude Opus 4.6 and surpassing all open-source comparators.

Analysis of tool usage demonstrates that ClinSeekAgent distillation teaches the student emergent procedural database reasoning, markedly increasing reliance on free-form SQL queries and diversifying retrieval policies beyond fixed templates.

Implications and Limitations

The practical implications are considerable: ClinSeekAgent operationalizes longitudinal, multimodal evidence-seeking, enabling clinical AI agents to interactively recover and integrate distributed data signals. This approach sharply improves prediction accuracy, especially in scenarios not captured by benchmark selection or templated context.

From a theoretical standpoint, ClinSeekAgent validates the transition from passive evidence consumption to active, tool-based acquisition as a necessary step for agentic clinical decision support. The distillation pipeline further opens scalable deployment avenues for next-generation open-source clinical agents, promoting reproducibility and democratization.

However, the current multimodal tasks are not sufficiently complex to fully stress-test long-horizon, compositional evidence-seeking. Training pipeline efficiency is bounded by the quality of teacher trajectories, which occasionally propagate tool call redundancies. More challenging benchmarks and trajectory optimization are recommended for future work.

Future Directions

Key speculative avenues include:

  • Enhanced trajectory filtering/compression or reinforcement learning to optimize evidence-seeking efficiency during training
  • Integration of more intricate task benchmarks requiring deeper cross-modal and temporal synthesis
  • Expansion of agentic reasoning to clinical contexts beyond critical care and imaging, targeting genomics, pathology, and real-time monitoring
  • Formal modularization of evidence-seeking policies for transfer across task families and domains

Conclusion

ClinSeekAgent introduces a paradigm-shifting agentic pipeline for automated multimodal evidence-seeking in clinical reasoning. Its dynamic framework enables agentic models to iteratively seek, refine, and synthesize heterogeneous evidence, yielding substantial improvements in risk prediction and complex multimodal benchmarks. As a training pipeline, it facilitates the distillation of emergent long-horizon evidence-seeking behavior into efficient open-source models. The work substantiates active evidence acquisition as a key direction for robust, flexible, and grounded clinical AI agents.

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What is this paper about?

This paper introduces ClinSeekAgent, a smart computer helper designed to support doctors. Instead of waiting for humans to hand it all the information, ClinSeekAgent acts like a careful detective: it searches for the right clues across many places—patient records, medical websites, and medical images—and then uses those clues to make better, grounded clinical decisions.

What questions are the researchers trying to answer?

The authors ask simple but important questions:

  • Can an AI system do better if it actively looks for evidence (like a detective) instead of passively reading a pre-made summary?
  • Can this “evidence-seeking” approach work across different kinds of data—text in electronic health records (EHR), chest X-ray images, and trustworthy medical websites?
  • Can they train smaller, open-source models to learn this detective-style behavior from stronger models?

How did they do it?

Think of a clinical question like a puzzle. Instead of giving the AI a pre-selected “folder” of clues, ClinSeekAgent gives the AI:

  • The patient’s ID and time point (so it doesn’t peek into the future),
  • Access to raw data sources, and
  • A toolbox of actions to gather evidence.

Here’s the idea in everyday terms:

  • Electronic Health Records (EHR): This is the patient’s digital chart (vitals, lab tests, medications, notes). ClinSeekAgent can “ask” the database precise questions to find the right parts of a patient’s history.
  • Web search: Like a student checking trusted sources to understand a definition or guideline.
  • Medical imaging tools: For chest X-rays, the system can use trained image tools (like a “specialist eye”) to detect findings and summarize what’s in the image.

The agent plans step-by-step: it chooses which tool to use next, reads what comes back, updates its thinking, and repeats until it has enough evidence to answer.

To test whether this helps, the authors built ClinSeek-Bench, which compares two modes for the same questions:

  • Curated Input: The model gets a pre-selected summary of relevant evidence (the usual way past benchmarks work).
  • Automated Evidence-Seeking: The model gets no summary—it must fetch the needed information itself using ClinSeekAgent’s tools.

They measured performance mainly with F1 score (0–100, higher is better), a common way to judge how accurate answers are.

They also used ClinSeekAgent as a training pipeline: they had a strong “teacher” model demonstrate good evidence-seeking steps and then taught a smaller open-source model to imitate those steps (this is called distillation).

What did they find, and why does it matter?

Main takeaways, explained simply:

  • Active searching helps—especially for harder tasks:
    • Text-only EHR tasks: Strong models did better when they could search the raw records themselves. For example, one top model’s overall score rose a few points when using ClinSeekAgent.
    • Multimodal tasks (mixing EHR and chest X-rays): Gains were bigger—one top model jumped by about 15 points. This suggests that actively combining image clues with patient history is very valuable.
  • Risk prediction shines: Tasks like predicting whether a patient will get sicker or how long they’ll stay often depend on scattered facts across the chart. ClinSeekAgent can hunt down these scattered clues better than a fixed pre-made summary.
  • Decision-making tasks were mixed: On some “what should we do next?” tasks, not all models improved. Sometimes the agent gathered too much irrelevant info or missed the key detail. In short, active searching helps most when the needed evidence is sparse and spread out, but it’s not a magic fix for every task.
  • Training smaller models to be better detectives works: By distilling the agent’s smart search steps into a compact open-source model (ClinSeek-35B-A3B), the authors boosted that model’s average score by around 12 points. It even approached the performance of a much stronger closed-source model on a standard agent benchmark. This is important because it means the approach can be shared openly and run more affordably.

Why this matters:

  • Real clinical work rarely comes as neat, pre-packaged summaries. A system that can find and fuse the right pieces itself is closer to how clinicians actually work.
  • Better use of multimodal data (text + images) can catch details that might be missed otherwise, potentially leading to safer and more accurate support tools.

What’s the bigger impact?

If clinical AI moves from “reading what it’s given” to “actively finding what it needs,” it could:

  • Make decisions that are better grounded in the full patient story,
  • Handle complex cases where key details are buried in long histories or images, and
  • Help smaller, open models learn strong habits for step-by-step evidence gathering.

That said, this is a research system, not a doctor. It still relies on model skills (planning, tool use, judgment), and it didn’t improve every task. But the results show a promising direction: teaching AI to be careful, curious, and evidence-driven—more like a good clinician collecting and verifying clues before deciding what to do.

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Knowledge Gaps

Knowledge gaps, limitations, and open questions

Below is a concise list of what remains missing, uncertain, or unexplored, framed to enable concrete follow-up work.

  • External validity and deployment: no evaluation in live clinical environments or prospectively collected EHRs; unknown performance, safety, and usability in real-time workflows.
  • Generalizability beyond MIMIC: all experiments rely on MIMIC-IV/CXR; portability to other health systems, outpatient settings, pediatrics, non-ICU care, and non-English EHRs is untested.
  • Interoperability: tools are tailored to local SQL over MIMIC schemas; no implementation or evaluation with standardized FHIR APIs or vendor-specific EHR interfaces.
  • Clinical safety and oversight: absence of safety guardrails, escalation policies, or clinician-in-the-loop mechanisms to prevent harmful actions when retrieved evidence is incomplete or web content is unreliable.
  • Uncertainty and calibration: risk predictions reported only via F1; no probability calibration (e.g., Brier score, ECE), AUROC/PR-AUC, or abstention strategies for high-stakes decisions.
  • Decision-making deficits: consistent degradations on decision-making subtasks are described but not addressed; no mitigation methods (e.g., focused retrieval, salience estimation, RL-based planning) are evaluated.
  • Tool ablations: no systematic ablation isolating contributions of EHR, imaging, and web tools; unclear which tools drive gains for which task families.
  • Retrieval quality metrics: lack of retrieval recall/precision, coverage, and relevance assessments for EHR queries and imaging tools; no audits of missed-but-relevant evidence.
  • Cost–efficiency and latency: no measurement of wall-clock latency, token/tool-call budgets, or API costs; real-time feasibility and budget-aware planning are unknown.
  • Stopping criteria and long-horizon control: no principled halting policy, search-depth control, or utility-based stopping; trade-offs between more evidence and error accumulation are unexplored.
  • Robustness to data issues: no tests under missingness, noisy timestamps, code/version drift, conflicting entries, or adversarial/inaccurate web content.
  • Temporal leakage audits: while post-cutoff data are purportedly hidden, there is no formal leakage audit across EHR joins, image/report linkages, or tool intermediates.
  • Fairness and bias: no subgroup analyses by age, sex, race/ethnicity, admission type, or comorbidity; equity impacts of tool-enabled retrieval are unassessed.
  • Interpretability and evidence attribution: trajectories are recorded but not evaluated for human interpretability, faithfulness, or clinician-auditable evidence attribution.
  • External knowledge provenance: browser sources, quality filters, and provenance tracking are unspecified; no verification against authoritative guidelines or handling of contradictory sources.
  • Imaging scope: imaging tools focus on chest X-ray; extension to CT, MRI, ultrasound, echocardiography, and waveforms is not supported or evaluated.
  • Image-tool reliability: no calibration/validation of the CXR classifier and grounding tools on the exact evaluation distribution; potential error propagation is not analyzed.
  • Compositional reasoning analysis: no fine-grained evaluation of how EHR, imaging, and web evidence are combined; lack of causal or diagnostic studies of failure modes in multimodal fusion.
  • Task–metric alignment: using sample-wise F1 for risk tasks deviates from standard clinical evaluation (AUROC, PR-AUC, decision-utility); downstream clinical utility is not measured.
  • Statistical rigor: results lack confidence intervals, significance tests, and variance estimates across resamples; small per-task samples (40) may induce instability.
  • Benchmark pairing parity: the agentic setting sometimes accesses more than the curated 24-hour window; parity and fairness of the paired comparisons need formalization and sensitivity checks.
  • Tool correctness guarantees: no safeguards for SQL correctness (time windows, joins, units), schema-evolution handling, or automated query validation/unit tests.
  • Planning competence characterization: unclear which “agentic” capabilities (e.g., tool grounding, memory, chain-of-thought control) are necessary/sufficient for gains; no diagnostic benchmarks of planning quality.
  • Learning paradigm: student is trained via SFT only; no exploration or reinforcement learning to optimize planning, retrieval focus, or stop conditions.
  • Data overlap risks: unclear whether training trajectories or tool models are exposed to evaluation distributions (e.g., via pretraining); formal data-contamination checks are missing.
  • Privacy and security: PHI handling, sandboxing of tools, auditability, and compliance (HIPAA/GDPR) are not discussed; risks from web queries and logging are unaddressed.
  • Versioning and drift: no strategy for keeping medical knowledge sources, ontologies, and tool models updated and version-controlled; impact of drift is unknown.
  • Human factors and UI: no user studies on how to present retrieved evidence, reduce cognitive load, or integrate agent outputs into clinical documentation/decision support.
  • Domain adaptation: no methods for rapid adaptation to site-specific vocabularies, coding systems (ICD/CPT/LOINC), formularies, or local practice patterns.
  • Negative evidence handling: the agent sometimes accumulates irrelevant evidence; no methods to detect and suppress distractors or prioritize high-yield signals.
  • Conflict resolution: no mechanisms to reconcile conflicting evidence across modalities/sources or to trigger secondary verification.
  • Multi-agent orchestration: single-agent approach only; potential benefits of specialist sub-agents (e.g., imaging, pharmacology, coding) and coordinators are unexplored.
  • Multimodal training: the distilled student is trained on text-based trajectories; effects of including multimodal (image + EHR + web) trajectories on downstream multimodal tasks are unknown.
  • Outcome-grounded evaluation: no assessment of clinical impact (e.g., time-to-diagnosis, error reduction) or decision-analytic measures (net benefit, cost-effectiveness).
  • Reproducibility and release: full code/data/model release is promised but not yet available; exact tool schemas, prompts, and environment seeds are needed for replication.
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Practical Applications

Practical Applications Derived from the Paper

The paper introduces ClinSeekAgent: an automated, multimodal evidence-seeking agent framework for clinical decision support that actively retrieves and synthesizes EHR, medical imaging, and external knowledge. It also provides ClinSeek-Bench (paired curated vs agentic evaluation) and a training pipeline that distills trajectories into a compact open-source model (ClinSeek-35B-A3B). The following applications leverage these findings, methods, and innovations.

Immediate Applications

The items below can be piloted now in controlled, human-in-the-loop settings, especially where structured EHR access and basic imaging pipelines already exist.

  • Clinical risk screening and rounding copilot (Sector: healthcare; hospital medicine, ICU, ED)
    • What: Deploy agentic risk prediction for in-hospital mortality, 24h decompensation, ED hospitalization likelihood, and length-of-stay forecasting; surface the evidence trail (vitals, labs, prior events) the agent retrieved.
    • Why now: ClinSeekAgent showed consistent gains on risk-prediction tasks and multimodal tasks; strongest improvements occur when evidence is sparse and longitudinal.
    • Tools/workflow: ehr.load_ehr, ehr.run_sql_query for temporal retrieval; optional CXR classifier for added context; evidence trajectory recorded for audit.
    • Assumptions/dependencies: Read-only EHR access (SQL or FHIR), time-cutoff enforcement to avoid leakage, clinician review, model guardrails, HIPAA-compliant deployment.
  • Radiology reading assistant with EHR-grounded CXR analysis (Sector: healthcare; radiology; imaging IT)
    • What: At read-time, agent retrieves relevant labs/notes, runs CXR finding detection and temporal comparison, drafts structured impressions, and highlights changes versus prior studies.
    • Why now: Large gains on CXR finding presence, enumeration, and change comparison under agentic tool use; compositional use of image classifiers + EHR queries drives improvements.
    • Tools/workflow: DICOM preprocessing, CXR classifier and change tools, ehr.run_sql_query for prior events, trajectory logging for explainability.
    • Assumptions/dependencies: PACS/VNA integration, validated CXR models for your data distribution, radiologist-in-the-loop, de-identification for model logs.
  • Evidence-seeking consult note enrichment (Sector: healthcare; internal medicine; care coordination)
    • What: Auto-compile a “case pack” for rounds or consults by pulling longitudinal vitals, key labs, meds, micro results, and imaging findings relevant to the clinical question.
    • Why now: Agent excels at finding sparse, decisive EHR signals and integrates multimodal context without requiring pre-curated inputs.
    • Tools/workflow: Schema inspection, candidate grounding, SQL queries, optional web lookup for definitions (e.g., phenotype taxonomies), export to EHR note templates.
    • Assumptions/dependencies: Schema mapping to local codes (ICD/LOINC/SNOMED), content validation; limit or disable external web sources if policy restricts.
  • Agentic cohort discovery and data curation for clinical research (Sector: academia; hospital research IT; pharma RWD)
    • What: Use the tool suite to programmatically identify cohorts, extract longitudinal features, and generate label candidates; capture trajectories for transparent inclusion/exclusion logic.
    • Why now: The agent treats EHR as a programmable DB (post-SFT, SQL usage rose 6x), enabling flexible phenotyping and feature building.
    • Tools/workflow: ehr.run_sql_query, schema inspection, trajectory logs as data lineage; export feature tables for modeling.
    • Assumptions/dependencies: IRB approval, data governance, reproducible query libraries, quality checks against gold phenotypes.
  • Multi-source CDS “second look” for antibiotic selection in ED/ICU (Sector: healthcare; pharmacy; ED operations)
    • What: When asked for a next-med suggestion (e.g., ED Pyxis), the agent pulls vitals, cultures, organ function, and searches guidelines to propose candidate antibiotics with rationale.
    • Why now: Paper showcases correct antibiotic suggestion when agent retrieves context that curated inputs lacked.
    • Tools/workflow: EHR vitals/meds/micro tools, optional browser tool to retrieve local formulary or evidence summaries; justification via trajectory.
    • Assumptions/dependencies: Institutional guidelines integration (prefer curated internal sources over open web), pharmacist oversight, tight safety guardrails.
  • Benchmarking and evaluation platform migration (Sector: academia; AI product teams; benchmarking consortia)
    • What: Adopt ClinSeek-Bench methodology to pair curated and agentic settings for any internal dataset; quantify the delta from active evidence acquisition.
    • Why now: The paired design isolates evidence-seeking gains; useful for model selection and procurement.
    • Tools/workflow: Convert curated tasks into agentic tasks by removing pre-selected context; evaluate F1 and audit trajectories.
    • Assumptions/dependencies: Access to raw EHR/imaging with timestamps; harmonized label spaces.
  • On-prem SFT kit for compact clinical agents (Sector: healthcare IT; AI vendors; cloud/on-prem)
    • What: Use the distillation pipeline to fine-tune an internal 7–35B model on local trajectories for privacy-preserving, lower-latency deployment.
    • Why now: ClinSeek-35B-A3B substantially outperforms its base and approaches closed-source performance; practical step for hospitals avoiding PHI egress.
    • Tools/workflow: Trajectory collection with a strong teacher (or curated human traces), SFT on a secure cluster, inference with tool adapters.
    • Assumptions/dependencies: Training compute, licensing of base models, dataset representativeness, MLOps for continuous evaluation.
  • Regulatory and clinical audit artifacts via agent “flight recorder” (Sector: health policy; compliance; quality assurance)
    • What: Use evidence-seeking trajectories as auditable provenance for CDS suggestions—who called which tool, when, with what parameters, and what evidence contributed.
    • Why now: The method natively logs tool calls; crucial for internal review, CAPA, and future SaMD submissions.
    • Tools/workflow: Immutable logs, versioned tool schemas, redaction of PHI in analytic copies, dashboards to surface rationale snippets.
    • Assumptions/dependencies: Logging policies, retention schedules, security controls; clinician-accessible summaries to avoid alert fatigue.
  • Education: case-based training on “how to seek evidence” (Sector: medical education; CME)
    • What: Use agent trajectories to teach residents and students to plan EHR queries, interpret CXRs with context, and justify decisions.
    • Why now: Paper shows models learn procedural search behavior (e.g., SQL query planning); human learners can benefit from the same structure.
    • Tools/workflow: Simulated EHR sandboxes, curated cases with masked “answer,” step-by-step trajectories, self-assessment modules.
    • Assumptions/dependencies: Educational EHR environments (e.g., synthetic MIMIC clones), faculty validation.

Long-Term Applications

The items below require further research, scaling, integration, or regulatory progress before routine deployment.

  • Real-time, multimodal CDS across modalities and waveforms (Sector: healthcare; ED/ICU; cardiology; neurology)
    • What: Extend from CXR to CT/MRI/echo/ECG/waveforms; unify agent planning over streaming vitals and imaging to support early sepsis, stroke, and decompensation interventions.
    • Dependencies: Expanded toolkits for non-CXR modalities, inference latency budgets, throughput scaling, robust device integration, prospective trials, SaMD pathway.
  • Closed-loop order set assistants and prior-auth automation (Sector: healthcare ops; revenue cycle; payers)
    • What: From suggestion to pre-populated order sets, prior-auth packets, and payer-aligned documentation with explainable evidence trails.
    • Dependencies: Vendor APIs for orders, payer policy ingestion, safety interlocks, human sign-off, thorough validation under institutional policy.
  • Patient-facing agent for personal health records (PHR) via SMART on FHIR (Sector: daily life; digital health)
    • What: With user consent, the agent actively retrieves labs, meds, imaging summaries from patient portals, explains longitudinal trends, prepares “questions for my doctor.”
    • Dependencies: Robust PHR access, clinically safe explanations, UI for uncertainty, non-device CDS regulatory guardrails, bias and readability tests.
  • Population health surveillance and care-gap closure (Sector: public health; payer-provider; ACOs)
    • What: Scan longitudinal EHRs to flag high-risk patients, missed screenings, and care gaps, prioritizing outreach with traceable evidence.
    • Dependencies: Interoperable data aggregation (HIEs/FHIR), fairness monitoring, opt-out/consent at scale, outcome-driven RL safely tuned.
  • Multi-agent clinical teamwork (Sector: healthcare; team-based care)
    • What: Coordinated agents specialized for radiology, pharmacy, nursing, and discharge planning that share evidence states and divide tool calls to reduce cognitive load.
    • Dependencies: Role-based access control, conflict resolution policies, orchestration frameworks, team-level evaluation metrics.
  • Federated and edge deployments with privacy-preserving training (Sector: software; cloud; healthcare IT)
    • What: Distill and fine-tune agent behaviors across hospitals without centralizing PHI; run compact models at the edge for low-latency, privacy-first CDS.
    • Dependencies: Federated SFT/adapter training, secure aggregation, policy-compliant telemetry, hardware accelerators on-prem.
  • Standardization of agent tool schemas and auditability (Sector: policy; standards bodies)
    • What: Establish interoperable “AgentOps” standards for tool-call schemas, provenance records, redaction, and replay; certify evidence-seeking capabilities.
    • Dependencies: Collaboration with HL7 (FHIR), DICOM WG, ONC, FDA; community adoption; reference implementations and conformance tests.
  • Outcome-aware training with safe reinforcement signals (Sector: academia; AI safety)
    • What: Move beyond SFT to outcome-aligned optimization (RLAIF/RLHF/RL from retrospective outcomes) while preserving safety and causal caution.
    • Dependencies: Carefully curated feedback signals, off-policy evaluation, counterfactual risk, clinical oversight, mitigation of reward hacking.
  • Cross-institutional benchmark standard (Sector: academia; consortia)
    • What: Expand ClinSeek-Bench to multi-hospital, multi-modality, multi-language settings; provide matched curated vs agentic task pairs as a community resource.
    • Dependencies: Data-sharing agreements, de-identification pipelines, governance, harmonized ontologies, reproducibility tooling.
  • Utilization management and claims QA (Sector: finance/insurance)
    • What: Evidence-seeking review of charts for coverage determinations, medical necessity, and audit support, with transparent trajectories to explain determinations.
    • Dependencies: Payer-provider data exchange, careful bias controls, adjudication workflows, regulatory alignment.
  • Clinician-in-the-loop automation for billing/coding and quality reporting (Sector: healthcare ops)
    • What: Agent extracts diagnoses/procedures, aligns to ICD/PCS/HCPCS, and assembles quality measures with justifications.
    • Dependencies: High-precision coding ontologies, audited accuracy thresholds, compliance sign-off, change management.
  • Trust calibration and explanation UX for CDS (Sector: software product; HCI)
    • What: Design interfaces that expose the agent’s trajectory, uncertainties, and alternative evidence paths to calibrate clinician trust and reduce over-reliance.
    • Dependencies: Human factors studies, standard risk labeling, EBM alignment, integration with existing CDS alert frameworks.

Cross-Cutting Assumptions and Dependencies

  • Data access and interoperability: Reliable, secure access to raw EHR (SQL/FHIR), PACS/DICOM, and institutional knowledge bases; robust code/ontology mapping (ICD/LOINC/SNOMED/ATC).
  • Safety, privacy, and compliance: HIPAA/GDPR compliance; PHI minimization; role-based access; auditable logs with appropriate redaction; adherence to Non-Device CDS vs SaMD requirements.
  • Model/agent capability: Strong planning and tool-use skills are crucial; weaker models may not benefit from agentic pipelines and could degrade on decision-making tasks.
  • Generalization and validation: MIMIC-trained insights may not transfer to local distributions; prospective, clinician-supervised evaluations are required before high-stakes use.
  • Tool reliability and provenance: Imaging classifiers and web sources must be vetted; prefer internally curated knowledge over open web for regulated contexts; maintain immutable provenance of tool outputs.
  • Workflow fit and human factors: Human-in-the-loop review, alert fatigue management, calibration of uncertainty, and clear ownership for final clinical decisions.
  • Infrastructure and cost: On-prem or VPC deployment for PHI; compute budgets for long trajectories and 52k-token contexts; monitoring for latency and throughput in real-time settings.

These applications collectively move clinical AI from passive context consumption toward active, auditable, multimodal evidence acquisition—offering immediate gains in risk prediction and radiology support and charting a path to broader, regulated, and interoperable CDS in the long term.

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Glossary

  • Agentic: Refers to LLM-driven systems that plan and act via tools to achieve goals, rather than passively consuming inputs. "an automated agentic framework for dynamic multimodal evidence seeking"
  • Anatomical segmentation: Image-processing task that delineates anatomical structures (e.g., lungs, heart) in medical images. "anatomical segmentation"
  • Answer schema: The structured format or set of rules specifying how a model should produce its final answer. "the answer schema or candidate label space when available"
  • Automated Evidence-Seeking: An evaluation/operation mode where the agent must autonomously find necessary data from raw sources before answering. "Automated Evidence-Seeking over raw clinical data"
  • Candidate label space: The predefined set of possible output labels for a prediction or classification task. "the answer schema or candidate label space when available"
  • Candidate-term grounding: Mapping task terms (e.g., drug or finding names) to database fields or ontology entries to enable precise retrieval. "candidate-term grounding"
  • Compositional tool use: Coordinated use of multiple tools in sequence or combination to integrate evidence across sources and modalities. "compositional tool use"
  • Curated Input: A setting where task-relevant evidence is preselected and packaged for the model prior to inference. "We preserve this original setting as Curated Input"
  • CXR: Abbreviation for chest X‑ray; used for imaging-based tasks and findings. "CXR finding presence"
  • Decompensation: Rapid clinical deterioration (e.g., within 24 hours) requiring prediction from EHR signals. "24-hour decompensation prediction"
  • DICOM preprocessing: Preparation of medical images in the DICOM standard for downstream analysis (e.g., normalization, extraction). "DICOM preprocessing"
  • EHR (Electronic Health Record): Longitudinal digital record of a patient’s health information, including structured tables and notes. "Electronic Health Record (EHR) tables"
  • EHR-CXR linkage: The association between a patient’s EHR entries and their chest X‑ray studies to enable multimodal reasoning. "valid EHR-CXR linkage"
  • Evidence-packaging process: Upfront selection and organization of task-relevant patient information prior to model inference. "These inputs reflect the evidence-packaging process of the source benchmarks"
  • Evidence-seeking trajectory: The recorded sequence of tool calls, observations, and decisions that lead an agent from query to answer. "evidence-seeking trajectory"
  • F1 score: Harmonic mean of precision and recall used as the primary performance metric, reported per sample or overall. "We report sample-wise F1(%) as the primary metric"
  • Harutyunyan-2019 taxonomy: A specific 25-phenotype clinical taxonomy used as an external knowledge reference in tasks. "the 25-phenotype Harutyunyan-2019 taxonomy"
  • Long-horizon tool use: Planning and executing multi-step, extended sequences of tool calls to gather sufficient evidence. "plan and execute long-horizon tool use"
  • MIMIC-CXR: A public dataset of de-identified chest radiographs paired with free-text reports. "MIMIC-CXR chest radiographs"
  • MIMIC-IV: A large, publicly available critical-care EHR dataset used for building and evaluating models. "MIMIC-IV EHRs"
  • Modality-specific metadata: Auxiliary information tied to a particular data modality (e.g., image paths for imaging tasks). "modality-specific metadata such as image paths"
  • Multimodal: Involving multiple data types (e.g., text, tabular EHR, images) integrated within one task or model. "multimodal evidence"
  • Parameter-efficiency tradeoff: The balance between model size (parameters) and performance, emphasizing efficiency. "parameter-efficiency tradeoff"
  • Phenotype prediction: Predicting clinical phenotypes (disease categories/traits) from EHR and/or imaging data. "phenotype prediction"
  • Phrase grounding: Linking textual phrases (e.g., “pleural effusion”) to corresponding regions or features in an image. "phrase grounding"
  • Piperacillin: A broad-spectrum antibiotic used here as a predicted ED medication based on retrieved evidence. "predict piperacillin"
  • Prediction cutoff: The time boundary before which all data are available for the model, preventing future information leakage. "prediction cutoff"
  • Reference timestamp: The designated time point at which the prediction is made and after which data are hidden. "reference timestamp or prediction time"
  • Risk prediction: Estimating the likelihood of future adverse events (e.g., mortality, decompensation) from available data. "risk-prediction tasks"
  • Schema inspection: Examining database schemas to understand available tables, columns, and relationships before querying. "schema inspection"
  • SQL-based querying: Using SQL to retrieve, filter, and aggregate structured EHR records programmatically. "SQL-based querying"
  • Temporal leakage: Using information that occurs after the prediction time, which can artificially inflate performance. "prevent temporal leakage"
  • Temporal retrieval: Time-aware extraction of events or measurements up to the prediction/reference time. "temporal retrieval"
  • Tool space: The set of available tools (EHR, web, imaging) an agent can invoke during evidence seeking. "ClinSeekAgent tool space"
  • Trajectory distillation: Training a smaller model using recorded tool-use trajectories from a stronger teacher model. "ClinSeekAgent trajectory distillation improves"
  • Patient vignettes: Concise, curated clinical case descriptions used in benchmarks but less reflective of raw workflows. "patient vignettes"
  • Radiographs: Medical X‑ray images (e.g., chest X‑rays) used for diagnostic evidence in multimodal tasks. "chest radiographs"
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