Articlenpj health systems2025
Synoptic reporting by summarizing cancer pathology reports using large language models.
Article in npj health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Practical considerations for pathologists when selecting digital pathology systems: an ESDIP guide.The Journal of pathology · 2026Review
- Large Language Models in Preclinical Spine Research: A Scoping Review and Expert Perspective on Evidence-Aware Experimental Workflows.JOR spine · 2026Article
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- Digital Registrar: A Schema-First Framework for Multi-Cancer Privacy-Preserving Pathology Abstraction via Local LLMs.Diagnostics (Basel, Switzerland) · 2026Article
- Toward Automating the Summarization of Cancer Pathology Reports Using Large Language Models to Improve Clinical Usability.JCO clinical cancer informatics · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Navigating the complexity of WHO CNS5: the evolutionary trajectory of glioma classification and the emergence of large language models.Frontiers in oncology · 2026Review
- From Zero-Shot to Bedside: A Practical Playbook for Adapting Open-Source Large Language Models to Clinical Symptom Extraction.Proceedings of machine learning research · 2026Article
- Developing a named entity framework for thyroid cancer staging and risk level classification using large language models.NPJ digital medicine · 2025Article
- Large language models in cancer: potentials, risks, and safeguards.BJR artificial intelligence · 2025Review
Corrections and comments
- Update of
Authors and funding
10 authors.
Funding
Abstract
Synoptic reporting, the documenting of clinical information in a structured manner, enhances patient care by improving accuracy, readability, and report completeness, but imposes significant administrative burdens on physicians. The potential of Large Language Models (LLMs) for automating synoptic reporting remains underexplored. In this study, we explore state-of-the-art LLMs for automatic synoptic reporting, using 7774 pathology reports from 8 cancer types, paired with physician annotated synoptic reports from the Mayo Clinic EHR. We developed a comprehensive automation framework, combining state-of-the-art LLMs, incorporating parameter-efficient optimization, scalable prompt templates, and robust evaluation strategies. We validate our results on both internal and external data, ensuring alignment with pathologist responses. Using our framework, fine-tuned LLAMA-2 achieved BERT F1 scores above 0.86 across all data elements and exceeding 0.94 over 50% (11 of 22) of the data elements, translating to manually assessed mean semantic accuracies of 77% and up to 81% for short clinical reports.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.