EviSearch: A Human in the Loop System for Extracting and Auditing Clinical Evidence for Systematic Reviews
- URL: http://arxiv.org/abs/2604.14165v1
- Date: Mon, 23 Mar 2026 22:36:54 GMT
- Title: EviSearch: A Human in the Loop System for Extracting and Auditing Clinical Evidence for Systematic Reviews
- Abstract summary: EviSearch is a multi-agent extraction system for clinical evidence tables.<n>The pipeline is designed for high-precision extraction across multimodal evidence sources.<n>EviSearch is intended to accelerate living systematic review, reduce manual curation burden, and provide a safe, auditable path for integrating LLM-based extraction into evidence synthesis pipelines.
- Score: 7.325762712182949
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present EviSearch, a multi-agent extraction system that automates the creation of ontology-aligned clinical evidence tables directly from native trial PDFs while guaranteeing per-cell provenance for audit and human verification. EviSearch pairs a PDF-query agent (which preserves rendered layout and figures) with a retrieval-guided search agent and a reconciliation module that forces page-level verification when agents disagree. The pipeline is designed for high-precision extraction across multimodal evidence sources (text, tables, figures) and for generating reviewer-actionable provenance that clinicians can inspect and correct. On a clinician-curated benchmark of oncology trial papers, EviSearch substantially improves extraction accuracy relative to strong parsed-text baselines while providing comprehensive attribution coverage. By logging reconciler decisions and reviewer edits, the system produces structured preference and supervision signals that bootstrap iterative model improvement. EviSearch is intended to accelerate living systematic review workflows, reduce manual curation burden, and provide a safe, auditable path for integrating LLM-based extraction into evidence synthesis pipelines.
Related papers
- RAPTOR+: A Visually Grounded Vision-Language Framework to Improve Clinical Trust and Auditability in Automated Cancer Referral Processing [1.384221236843682]
We present RAPTOR+, a multimodal extension that uses Vision-Language Models (VLMs) for end-to-end referral understanding.<n>We evaluate fine-tuned VLMs, commercial and open-source zero-shot VLMs, and the original OCR-based pipeline on 223 clinically curated CRC urgent referral forms.
arXiv Detail & Related papers (2026-05-25T15:30:02Z) - ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning [49.05361955143476]
ClinSeekAgent is an automated agentic framework for dynamic multimodal evidence seeking.<n>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.
arXiv Detail & Related papers (2026-05-19T17:58:37Z) - 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) - Towards Trustworthy Depression Estimation via Disentangled Evidential Learning [50.22167852149165]
We propose EviDep, an evidential learning framework that jointly quantifies depression severity.<n>EviDep enforces strict information integrity to guarantee robust evidence synthesis.<n>It achieves state-of-the-art predictive accuracy and superior uncertainty calibration, delivering a robust fail-safe mechanism for trustworthy clinical screening.
arXiv Detail & Related papers (2026-04-17T13:27:11Z) - Automated Auditing of Hospital Discharge Summaries for Care Transitions [2.1443570696048906]
We propose an automated framework for large-scale auditing of discharge summaries using Large Language Models (LLMs)<n>Our approach operationalizes core transition-of-care requirements into a structured validation checklist of questions based on DISCHARGED framework.<n>This work demonstrates the feasibility of scalable, automated clinical auditing and provides a foundation for systematic quality improvement in electronic health record documentation.
arXiv Detail & Related papers (2026-04-07T05:04:00Z) - UltrasoundAgents: Hierarchical Multi-Agent Evidence-Chain Reasoning for Breast Ultrasound Diagnosis [14.027321451902734]
We propose a hierarchical multi-agent framework, termed UltrasoundAgents.<n>A main agent localizes the lesion in the full image and triggers a crop-and-zoom operation.<n>A sub-agent analyzes the local view and predicts four clinically relevant attributes, namely echogenicity pattern, calcification, boundary type, and edge morphology.<n>The main agent then integrates these structured attributes to perform evidence-based reasoning and output the BI-RADS category and the malignancy prediction, while producing reviewable intermediate evidence.
arXiv Detail & Related papers (2026-03-11T15:03:52Z) - Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification [60.18369393468405]
Existing verifiers usually underperform owing to a lack of domain knowledge and limited calibration.<n>GLEAN compiles expert-curated protocols into trajectory-informed, well-calibrated correctness signals.<n>We empirically validate GLEAN with agentic clinical diagnosis across three diseases from the MIMIC-IV dataset.
arXiv Detail & Related papers (2026-03-03T09:36:43Z) - Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification [71.98473277917962]
Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving.<n>We propose an alternative paradigm: self-evolving the agent's ability by iteratively verifying the policy model's outputs, guided by meticulously crafted rubrics.<n>We present DeepVerifier, a rubrics-based outcome reward verifier that leverages the asymmetry of verification.
arXiv Detail & Related papers (2026-01-22T09:47:31Z) - From Chaos to Clarity: Schema-Constrained AI for Auditable Biomedical Evidence Extraction from Full-Text PDFs [2.136797327390818]
Existing document AI systems are limited by OCR errors, long-document fragmentation, constrained throughput, and insufficient auditability for high-stakes synthesis.<n>We present a schema-constrained AI extraction system that transforms full-text biomedical PDFs into structured, analysis-ready records.
arXiv Detail & Related papers (2025-12-31T00:43:53Z) - Leveraging LLMs for Structured Data Extraction from Unstructured Patient Records [0.0]
Manual chart review remains an extremely time-consuming and resource-intensive component of clinical research.<n>We present a framework for automated structured feature extraction from clinical notes leveraging locally deployed large language models (LLMs)<n>This framework demonstrates the potential of LLM systems to reduce the burden of manual chart review and increase consistency in data capture.
arXiv Detail & Related papers (2025-12-03T14:10:12Z) - Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
arXiv Detail & Related papers (2025-04-21T04:56:47Z) - Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning.
They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health.
Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
arXiv Detail & Related papers (2023-05-30T22:05:11Z) - Detecting automatically the layout of clinical documents to enhance the
performances of downstream natural language processing [53.797797404164946]
We designed an algorithm to process clinical PDF documents and extract only clinically relevant text.
The algorithm consists of several steps: initial text extraction using a PDF, followed by classification into such categories as body text, left notes, and footers.
Medical performance was evaluated by examining the extraction of medical concepts of interest from the text in their respective sections.
arXiv Detail & Related papers (2023-05-23T08:38:33Z) - Sent2Span: Span Detection for PICO Extraction in the Biomedical Text
without Span Annotations [17.480599429324155]
Population, Intervention, Comparator, and Outcome (PICO) information from clinical trial articles may be an effective way to automatically assign trials to systematic reviews.
We propose and test a novel approach to PICO span detection using only crowdsourced sentence-level annotations.
Experiments on two datasets show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods.
arXiv Detail & Related papers (2021-09-06T06:14:49Z)
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.