mlsanityData to 13 Oct 2026

PathoScribe: Transforming Pathology Data into a Living Library with a Unified LLM-Driven Framework for Semantic Retrieval and Clinical Integration

arXiv 2603.08935 ↗First posted 9 Mar 2026cs.CV cs.AI cs.CLcited by 0

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Abstract (arXiv)

Pathology underpins modern diagnosis and cancer care, yet its most valuable asset, the accumulated experience encoded in millions of narrative reports, remains largely inaccessible. Although institutions are rapidly digitizing pathology workflows, storing data without effective mechanisms for retrieval and reasoning risks transforming archives into a passive data repository, where institutional knowledge exists but cannot meaningfully inform patient care. True progress requires not only digitization, but the ability for pathologists to interrogate prior similar cases in real time while evaluating a new diagnostic dilemma. We present PathoScribe, a unified retrieval-augmented large language model (LLM) framework designed to transform static pathology archives into a searchable, reasoning-enabled living library. PathoScribe enables natural language case exploration, automated cohort construction, clinical question answering, immunohistochemistry (IHC) panel recommendation, and prompt-controlled report transformation within a single architecture. Evaluated on 70,000 multi-institutional surgical pathology reports, PathoScribe achieved perfect Recall@10 for natural language case retrieval and demonstrated high-quality retrieval-grounded reasoning (mean reviewer score 4.56/5). Critically, the system operationalized automated cohort construction from free-text eligibility criteria, assembling research-ready cohorts in minutes (mean 9.2 minutes) with 91.3% agreement to human reviewers and no eligible cases incorrectly excluded, representing orders-of-magnitude reductions in time and cost compared to traditional manual chart review. This work establishes a scalable foundation for converting digital pathology archives from passive storage systems into active clinical intelligence platforms.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 18 of its 53 references that are in the map. First posted on arXiv 9 Mar 2026.

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References · 18

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,221
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,797
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  5. 2022Large Language Models Encode Clinical Knowledgecited by 1,817
  6. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,229
  7. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 739
  8. 2022GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Recordscited by 238
  9. 2023A Study of Generative Large Language Model for Medical Research and Healthcarecited by 162
  10. 2022An Analysis of Fusion Functions for Hybrid Retrievalcited by 50
  11. 2022PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domaincited by 12
  12. 2025HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathologycited by 12
6 more
  1. 2024Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Toolcited by 6
  2. 2023Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide imagescited by 5
  3. 2023Cross-attention-based saliency inference for predicting cancer metastasis on whole slide imagescited by 5
  4. 2026RANGER: Sparsely-Gated Mixture-of-Experts with Adaptive Retrieval Re-ranking for Pathology Report Generationcited by 2
  5. 2025Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patientscited by 1
  6. 2025Morphology-Aware Prognostic model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole Slide Imagescited by 1

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  1. 2025YpathRAG:A Retrieval-Augmented Generation Framework and Benchmark for Pathology
  2. 2025Knowledge Elicitation with Large Language Models for Interpretable Cancer Stage Identification from Pathology Reports
  3. 2026PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs
  4. 2026PathoArgus: Advancing Evidence-Grounded Long-Context Visual Reasoning across Gigapixel Whole-Slide and Multi-Slide Case Contexts
  5. 2026MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval
  6. 2025A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases
  7. 2026PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
  8. 2025ChatEXAONEPath: An Expert-level Multimodal Large Language Model for Histopathology Using Whole Slide Images