ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning".

Jane: Here's the gist of the paper. The gist The ClinSeekAgent framework introduces an automated agentic pipeline for dynamic multimodal evidence seeking in clinical decision support, shifting from passive evidence consumption to active acquisition.

Tom: How it works ClinSeekAgent is designed to gather 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 It 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 The framework differs from existing curated-evidence pipelines in that it does not passively consume a fixed evidence package prepared before inference Instead, given a clinical query and access to heterogeneous clinical data sources, ClinSeekAgent actively gathers evidence through (1) web search, (2) raw EHR retrieval, and (3) medical imaging tools This enables the agent to recover patient-specific, multimodal, and external medical signals that fixed curated contexts may miss Validation and Performance The effectiveness of ClinSeekAgent was validated through two primary methods.

Jane: First, in inference-time validation using ClinSeek-Bench, the authors constructed a suite that pairs Curated Input reasoning from fixed pre-selected evidence with Automated Evidence-Seeking over raw clinical data On text-only EHR tasks, Claude Opus 4.6 improved from 60.0 to 63.2 overall F1 under Automated Evidence-Seeking, and MiniMax M2.5 improved from 43.1 to 47.3 On multimodal tasks, ClinSeekAgent improves Claude Opus 4.6 from 47.5 to 62.6 (+15.1) Furthermore, when validated as a training pipeline, distilling agentic evidence-seeking trajectories into ClinSeek-35B-A3B achieved an average F1 of 34.0 on existing AgentEHR-Bench, improving over its Qwen3.5-35B-A3B baseline by +11.9 points Tool Space and Trajectory ClinSeekAgent exposes a unified tool space with 20 tools across three complementary evidence sources: EHR retrieval, web search, and medical image analysis The tool space includes 11 EHR tools for accessing patient-specific longitudinal records, browser tools for acquiring external medical knowledge through web search, and 6 image tools for extracting visual evidence through DICOM preprocessing, chest X-ray classification, report generation, phrase grounding, and anatomical segmentation ClinSeekAgent represents each run as an open-ended evidence-seeking trajectory τ = x, (ak, ok) K k=1 yˆ where x is the task instance and ak is a tool action Unlike rule-based retrieval pipelines, ClinSeekAgent does not impose an ordering over evidence sources Key Findings and Limitations The analysis revealed that the advantage of ClinSeekAgent is most pronounced in risk prediction, where 7 out of 9 evaluated models achieve a positive average gain on risk prediction when using ClinSeekAgent The gains are particularly pronounced on long-horizon hospital-event prediction tasks for Claude Opus 4.6, which substantially improves three tasks including Mortality Hospital by +12.5 points and LengthOfStay by +16.2 points Conversely, the main weakness of ClinSeekAgent appears in the decision-making task group, where subtasks show less consistent improvements and often degrade under the ClinSeek pipeline A limitation noted is that current multimodal evaluation tasks are still relatively simple in many cases, which does not fully stress-test the long-horizon multimodal evidence-seeking capability that ClinSeekAgent is designed for Additionally, the training pipeline relies primarily on supervised fine-tuning over teacher-generated trajectories, and trajectories produced by the teacher model are not always tool-efficient Moving from passive evidence consumption to active evidence acquisition is a promising direction for building more flexible, grounded, and capable clinical AI agents ClinSeekAgent demonstrates that agentic access to patient information is especially valuable when clinical decisions require jointly integrating EHR context and multimodal evidence The framework can serve as both an inference-time pipeline and a scalable training pipeline for distilling clinical evidence-seeking behavior into open-source models The results suggest that the paradigm gap is task-family-specific, as ClinSeek-style retrieval is highly effective for risk prediction but sometimes fails to find the critical information for decision making The paper concludes that ClinSeekAgent provides a unified multimodal evidence-seeking pipeline over raw EHR tables, medical image analysis tools, and external knowledge sources The research demonstrates that active evidence acquisition is a promising direction for building more flexible, grounded, and capable clinical AI agents.

Title and authors: Tom: Okay, so we're talking about "ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning" by Wu, Zhang, Wang, Tu, Hardy Chen, Zijun Wang Cihang Xie Yuyin Zhou. The authors are clearly focused on building this agentic pipeline for clinical decision support.

Jane: It sounds like the paper is proposing a framework that lets an AI agent do something much more realistic in a hospital setting where you can't just hand it one perfect summary of everything beforehand.

Lu: The title emphasizes "Automating Multimodal Evidence Seeking," which points to this unified system that handles EHRs, knowledge bases, and imaging tools all at once, rather than treating them as separate steps.

Meng: From what I'm seeing in the abstract, the key distinction is that it doesn't rely on a fixed evidence package; it gives the agent a clinical query and lets it actively decide what evidence to retrieve from whatever sources are available.

Lalam: So, instead of just reading a pre-packaged context, this agent plans its own search path across different data types based on the specific clinical question it's trying to answer.

Tom: Right. It’s about the agent being flexible enough to decide *where* and *what* evidence is needed to ground its final decision in a way that a static context just can't achieve.

The paper's summary: Jane: So, the paper summarizes ClinSeekAgent as an automated pipeline where the agent queries medical knowledge bases, navigates raw EHRs, and invokes medical imaging tools to gather evidence. Then it refines its hypotheses as new information comes in and integrates everything into a grounded clinical decision.

Tom: It really boils down to this: you start with a query, and the agent is responsible for gathering all the relevant patient-specific, multimodal signals—like text from records and visual data from scans—and using that to make its final call.

Lu: What I find important is how it frames this as an inference-time agent but also as something you can use for training. It serves two purposes: making decisions during inference and distilling the successful evidence-seeking steps into smaller, open-source models.

Meng: The paper shows that this active acquisition approach lets the agent recover signals that are often sparse or distributed across different parts of the record, which is a real problem when dealing with complex patient data.

Lalam: It’s like instead of having one giant file, the agent has a toolkit and it uses those tools intelligently to pull out exactly what's relevant for that specific moment in reasoning.

Jane: That makes sense. So it's about dynamic evidence gathering—the system is designed to be adaptive based on the query itself, not just following a pre-set route through the data.

The paper's improvements: Tom: The authors show some concrete improvements in their inference-time experiments, specifically when they compare ClinSeekAgent against fixed curated inputs. For text-only EHR tasks, Claude Opus four point six saw an F1 score improve from sixty point zero to sixty-three point two under the Automated Evidence-Seeking method alone <ref:2605.20176#pg1>.

Lu: And that gain is pretty substantial when you look at the multimodal tasks where it really shines; for that benchmark, ClinSeekAgent improved five out of six evaluated models, with Claude Opus four point six jumping from forty-seven point five to an overall F1 of sixty-two point six on those tasks under Automated Evidence-Seeking <ref:2605.20176#pg1>.

Jane: So what this means for us is that active evidence acquisition can actually recover clinical signals that a fixed curated context just misses, especially when the evidence is sparse or spread out across different data types like text and images.

Meng: The paper also shows it can be distilled into smaller models, like ClinSeek-35B-A3B <ref:2605.20176#pg1>. That model achieved an average F1 of thirty-four point zero on the AgentEHR-Bench, which was an improvement over its Qwen3 point 5-35B-A3B baseline by eleven point nine points in that distillation setup <ref:2605.20176#pg1>.

Lalam: That shows that the behavior the agent learns to seek evidence is something you can actually capture and transfer to a smaller model for use, which is important for deployment at scale.

Conclusion: Jane: So, to wrap up, ClinSeekAgent demonstrates that moving from passive evidence consumption to active acquisition of data through querying EHRs and imaging tools is a promising direction for clinical AI. It shows that agentic access to patient information is especially valuable when decisions need to integrate both structured records and multimodal evidence.

Tom: It’s definitely powerful in risk prediction; seven out of nine evaluated models showed a positive average gain on risk prediction when using ClinSeekAgent, with some long-horizon tasks like Mortality Hospital improving by twelve point five points for Claude Opus four point six <ref:2605.20176#pg1>.

Lu: But the paper is also careful about where it struggles, noting that the main weakness seems to be in the decision-making task group, where improvements aren't as consistent and sometimes even degrade under this evidence-seeking pipeline.

Meng: And they flagged a limitation: current multimodal evaluation tasks are still relatively simple in many cases, which means the framework might not fully stress-test its long-horizon multimodal capabilities yet. Plus, the training pipeline relies heavily on supervised fine-tuning over teacher-generated trajectories, and those teacher trajectories aren't always tool-efficient.

Lalam: So the big picture is that this framework provides a unified way to handle raw EHR tables, medical image tools, and external knowledge sources for evidence seeking. It’s a strong pipeline for inference and a scalable training tool too.

Jane: That’s the essence of ClinSeekAgent—active acquisition over passive consumption—and it suggests that the advantage of this approach is task-family specific; it works really well for risk prediction, but sometimes it doesn't find the critical information needed for complex decision making.

Tom: It’s a clear direction for building more flexible and grounded clinical AI agents, moving toward systems that actively gather the necessary inputs rather than just being fed static contexts.

Jane: That’s what we had with ClinSeekAgent, and it opens up a lot of possibilities for how we think about building agents that can genuinely reason over complex medical data.