Evaluating Agentic Harness Systems for Autonomous Computational Pathology

• URL: http://arxiv.org/abs/2607.02598v1
• Date: Wed, 01 Jul 2026 10:13:13 GMT
• Title: Evaluating Agentic Harness Systems for Autonomous Computational Pathology
• Abstract summary: ACP-Bench is a framework that adapts general agentic harness systems from computational pathology support toward ACP workflow capability.<n>ACP-Bench evaluates 41 pathology workflow tasks, including 24 biomarker, 7 morphology and 10 prognosis tasks spanning 6 body-system groups and 9 endpoint families.
• Score: 19.24356620524394
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Autonomous computational pathology (ACP) converts high-level pathology analysis goals into executable, traceable and clinically bounded workflows. Realizing this capability requires adapting general agentic harness systems to pathology-specific tasks, tools, evidence standards and clinical claim boundaries. We contribute ACP-Bench, a framework that adapts existing harness systems from computational pathology support toward ACP workflow capability. ACP-Bench evaluates 41 pathology workflow tasks, including 24 biomarker, 7 morphology and 10 prognosis tasks spanning 6 body-system groups and 9 endpoint families. The benchmark evaluates 9 models and 3 harness groups (Claude Code, Codex and Open Code), yielding 369 complete trajectories. ACP-Bench evaluates each trajectory across workflow execution, diagnostic performance and clinical-boundary alignment, combining expert-adjudicated process audits, diagnostic assessment and pathologist-validated safety review. Across evaluated systems, workflow initiation, task interpretation and diagnostic reporting were more mature than tool-bound execution, result binding and reflective workflow revision, and formal end-to-end completion remained rare. ACP-Bench provides a reusable standard for auditing whether agentic systems can operationalize pathology workflows before claims of reliable clinical autonomy.

Related papers

• STEP: A Modular Silent Trial Engine for Operational Evaluation of Digital Pathology AI in Routine Workflow [1.6877591150411722]
  Silent trials provide a bridge between retrospective validation of AI models and their use in clinical care.<n>We developed a reusable software platform for orchestrating prospective silent trials of computational pathology AI models.
  arXiv  Detail & Related papers  (2026-08-27T20:33:39Z)
• FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization [0.0]
  This paper presents FMRP-LEAN, a HIPAA-compliant, AI-augmented Laboratory Information Management System (LIMS) architecture that formalizes bio-specimen lifecycle management through a finite-state workflow model with explicit transition guards and dwell-time observability.
  arXiv  Detail & Related papers  (2026-07-22T17:11:52Z)
• Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology [54.42160154368719]
  Whole-slide images (WSI) have enabled the digital transformation of pathology.<n> multimodal models that jointly analyze histopathology images and pathology reports have shown promising potential for automated pathology report generation and AI-assisted diagnosis.<n>Here, we introduce TUM-Uteria, a uterine pathology dataset comprising WSIs paired with diagnostic pathology reports at both the case and slide levels.
  arXiv  Detail & Related papers  (2026-07-04T20:34:59Z)
• EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs [51.129595320595094]
  Clinical decision-making (CDM) is central to real-world clinical, where clinicians infer diagnoses, select treatments, or anticipate future health outcomes under incomplete evidence.<n>LLM models are increasingly used to support these decisions due to strong language capabilities, broad biomedical knowledge, and efficiency.<n>The reliability of LLMs on real-world clinical decision tasks remains insufficiently understood.
  arXiv  Detail & Related papers  (2026-05-28T22:38:26Z)
• From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction [61.12883122613684]
  This Review focuses on intervention-aware disease trajectory modeling in clinical AI--methods estimating patient-specific longitudinal disease evolution and assessing trajectory changes under alternative treatments.<n>We organize the field around six linked components: three decision tasks (factual forecasting, counterfactual estimation, policy evaluation) and three data-generating mechanisms (disease evolution, treatment assignment, observation process) that determine identifiability.<n>We present the first unified framework bridging forecasting, counterfactual trajectories, and policy evaluation across discrete/continuous time, explicitly addressing treatment assignment, time-varying confounding, and observation bias.
  arXiv  Detail & Related papers  (2026-05-16T10:45:26Z)
• CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents [17.491888826944074]
  We introduce CodeClinic, a benchmark for evaluating whether LLM agents can synthesize and compose reusable clinical skills.<n>We propose an offline autoformalization pipeline that converts natural-language clinical guidelines into reusable and verified Python skill libraries.
  arXiv  Detail & Related papers  (2026-05-10T17:45:01Z)
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
  We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
  arXiv  Detail & Related papers  (2026-05-10T06:30:19Z)
• MedCollab: Causal-Driven Multi-Agent Collaboration for Full-Cycle Clinical Diagnosis via IBIS-Structured Argumentation [6.334763475104128]
  We present MedCollab, a novel multi-agent framework that emulates the hierarchical consultation workflow of modern hospitals.<n>The framework incorporates a dynamic specialist recruitment mechanism that adaptively assembles clinical and examination agents according to patient-specific symptoms and examination results.
  arXiv  Detail & Related papers  (2026-03-01T14:25:58Z)
• AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning [73.50200033931148]
  We introduce AgentsEval, a multi-agent stream reasoning framework that emulates the collaborative diagnostic workflow of radiologists.<n>By dividing the evaluation process into interpretable steps including criteria definition, evidence extraction, alignment, and consistency scoring, AgentsEval provides explicit reasoning traces and structured clinical feedback.<n> Experimental results demonstrate that AgentsEval delivers clinically aligned, semantically faithful, and interpretable evaluations that remain robust under paraphrastic, semantic, and stylistic perturbations.
  arXiv  Detail & Related papers  (2026-01-23T11:59:13Z)
• From Performance to Practice: Knowledge-Distilled Segmentator for On-Premises Clinical Workflows [8.438143117044795]
  We present a deployment-oriented framework to translate a high-performing segmentation model into a scalable family of compact student models.<n>The proposed approach preserves architectural compatibility with existing clinical systems while enabling systematic capacity reduction.<n>Results show that knowledge distillation provides a practical and reliable pathway for converting research-grade segmentation models into maintainable, deployment-ready components.
  arXiv  Detail & Related papers  (2026-01-14T05:44:30Z)
• CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment [29.48544328813161]
  We introduce CP-Env, a controllable agentic hospital environment designed to evaluate large language models (LLMs) across end-to-end clinical pathways.<n>Following real hospital adaptive flow of healthcare, it enables branching, long-horizon task execution.<n>Results reveal that most models struggle with pathway hallucinations, exhibiting complexity and losing critical diagnostic details.
  arXiv  Detail & Related papers  (2025-12-11T01:54:55Z)
• CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation [62.0150409256153]
  We propose CXRAgent, a director-orchestrated, multi-stage agent for CXR interpretation.<n>The agent strategically orchestrates a set of CXR-analysis tools, with outputs normalized and verified by the Evidence-driven Validator.<n>Experiments on various CXR interpretation tasks show that CXRAgent delivers strong performance, providing visual evidence and generalizes well to clinical tasks of different complexity.
  arXiv  Detail & Related papers  (2025-10-24T10:31:30Z)
• RadFabric: Agentic AI System with Reasoning Capability for Radiology [61.25593938175618]
  RadFabric is a multi agent, multimodal reasoning framework that unifies visual and textual analysis for comprehensive CXR interpretation.<n>System employs specialized CXR agents for pathology detection, an Anatomical Interpretation Agent to map visual findings to precise anatomical structures, and a Reasoning Agent powered by large multimodal reasoning models to synthesize visual, anatomical, and clinical data into transparent and evidence based diagnoses.
  arXiv  Detail & Related papers  (2025-06-17T03:10:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.