ArticleNature communications2025
Generating dermatopathology reports from gigapixel whole slide images with HistoGPT.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness, Safety, and Workflow Burden of Large Language Model-Based Medical Report Generation: Systematic Review.Journal of medical Internet research · 2026Pooled it
- SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types.Nature cancer · 2026Article
- The Evolving Role of Artificial Intelligence in Dermatology: A Meta-Analysis of Diagnostic Performance, Clinical Applications, and Implementation Challenges (2003-2025).Diagnostics (Basel, Switzerland) · 2026Review
- From Label-Free Multiphoton Imaging to Pathological Reports: A Vision-Language Breast Cancer Margin Pathological Diagnosis System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- A deep learning framework for efficient pathology image analysis.Nature communications · 2026Article
- Digital Dermatology.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Review
- A disease-centric vision-language foundation model for precision oncology in kidney cancer.Nature communications · 2026Article
- An agentic framework for autonomous scientific discovery in cancer pathology.Nature medicine · 2026Article
- Decoding (digital) histopathology: The building blocks for computational researchers.PLOS digital health · 2026Review
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- Weakly supervised deep learning for cutaneous squamous and basal cell carcinoma in whole-slide histopathology.The journal of pathology. Clinical research · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Foundation models: Insights and implications for gastrointestinal cancer.World journal of gastroenterology · 2025Review
- MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling.NPJ digital medicine · 2025Article
- From Slide to Insight: The Emerging Alliance of Digital Pathology and AI in Melanoma Diagnostics.Cancers · 2025Review
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Multi-Modal Foundation Models for Computational Pathology: A Survey.Transactions on machine learning research · 2025Article
- Deep Learning Image Processing Models in Dermatopathology.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
26 authors.
Funding
Abstract
Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples under a microscope and summarizing the findings in a comprehensive pathology report is time-consuming, labor-intensive, and non-standardized. To address this problem, we present HistoGPT, a vision language model that generates pathology reports from a patient's multiple full-resolution histology images. It is trained on 15,129 whole slide images from 6705 dermatology patients with corresponding pathology reports. The generated reports match the quality of human-written reports for common and homogeneous malignancies, as confirmed by natural language processing metrics and domain expert analysis. We evaluate HistoGPT in an international, multi-center clinical study and show that it can accurately predict tumor subtypes, tumor thickness, and tumor margins in a zero-shot fashion. Our model demonstrates the potential of artificial intelligence to assist pathologists in evaluating, reporting, and understanding routine dermatopathology cases.
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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.