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Health·P Space·Evidence-backed problem·Published 2026-09-03

SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types

Abstract: Multimodal artificial intelligence, albeit showing great potential in computational pathology, remains limited to isolated patch-level interpretation and often fails to analyze gigapixel-scale whole-slide images (WSIs) essential for clinical utility. Here we present SlideChat, a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types. SlideChat integrates patch-level and slide-level pathology encoders with a pretrained large language model. Using 274,23…

TRV-2026-0973Peer-reviewedPermanent record — cite & verify
SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types

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The quick read

Multimodal artificial intelligence, albeit showing great potential in computational pathology, remains limited to isolated patch-level interpretation and often fails to analyze gigapixel-scale whole-slide images (WSIs) essential for clinical utility. Here we present SlideChat, a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types.

SlideChat integrates patch-level and slide-level pathology encoders with a pretrained large language model. Evaluated on 8,836 closed-ended questions, 129 open-ended questions and 3,149 WSI reports from five cohorts spanning 31 cancer types, SlideChat outperformed leading baselines by 19.1% in closed-ended accuracy and by 7.7% in report-generation Metric for Evaluation of Translation with Explicit Ordering score and received the highest expert ratings across five dimensions in open-ended question answering, showing the potential to enhance diagnostic workflows, medical education and clinical decision-making.

Main points
  • Multimodal artificial intelligence, albeit showing great potential in computational pathology, remains limited to isolated patch-level interpretation and often fails to analyze gigapixel-scale whole-slide images (WSIs) essential for clinical utility.
  • Here we present SlideChat, a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types.
  • SlideChat integrates patch-level and slide-level pathology encoders with a pretrained large language model.
Problem

Multimodal artificial intelligence, albeit showing great potential in computational pathology, remains limited to isolated patch-level interpretation and often fails to analyze gigapixel-scale whole-slide images (WSIs) essential for clinical utility.

The rundown

SlideChat integrates patch-level and slide-level pathology encoders with a pretrained large language model. Using 274,233 multimodal instruction samples, SlideChat is trained to learn the associations between WSIs and diagnostic reports and interpret complex queries in clinical practice.

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