Evidence map›Paper›PMID 42680939›Full record

ArticleNature cancer2026

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

Ying Chen, Chenglong Ma, Qiongqiong Li, Fang Yan, Yirong Chen, Tianbin Li, Jin Ye, Ming Hu, Yuxiang Lin, Yanjun Li and 14 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Ying Chen *Shanghai Artificial Intelligence Laboratory, Shanghai, China.
Chenglong Ma *Shanghai Artificial Intelligence Laboratory, Shanghai, China.ORCID http://orcid.org/0000-0002-1632-2480
Qiongqiong LiDepartment of Pathology, Shanghai General Hospital, Shanghai, China.
Fang YanShanghai Artificial Intelligence Laboratory, Shanghai, China.ORCID http://orcid.org/0009-0004-4216-3876
Yirong ChenShanghai Artificial Intelligence Laboratory, Shanghai, China.
Tianbin LiShanghai Artificial Intelligence Laboratory, Shanghai, China.
Jin YeShanghai Artificial Intelligence Laboratory, Shanghai, China.
Ming HuShanghai Artificial Intelligence Laboratory, Shanghai, China.
Yuxiang LinNational Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.ORCID http://orcid.org/0000-0001-8854-2995
Yanjun LiShanghai Artificial Intelligence Laboratory, Shanghai, China.
Guoan WangShanghai Artificial Intelligence Laboratory, Shanghai, China.
Huihui XuShanghai Artificial Intelligence Laboratory, Shanghai, China.
Hui DongDepartment of Pathology, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China.
Xiang WangSecond Department of Biliary Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China.
Xiaoxiao XuDepartment of Pathology, Shanghai General Hospital, Shanghai, China.
Yanyan ZhouDepartment of Pathology, Shanghai General Hospital, Shanghai, China.
Xia ZhuDepartment of Pathology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
Sen YangDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-0639-4122
Xiyue WangDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-3597-9090
Lu ZhangDepartment of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Yu QiaoShanghai Artificial Intelligence Laboratory, Shanghai, China.
Rongshan YuSchool of Informatics, Xiamen University, Xiamen, China. rsyu@xmu.edu.cn.ORCID http://orcid.org/0000-0003-2179-173X
Junjun HeShanghai Artificial Intelligence Laboratory, Shanghai, China. hejunjun@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-1813-1784
Yuanfeng JiDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA. yfj@stanford.edu.ORCID http://orcid.org/0000-0001-6799-5493

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 274,233 multimodal instruction samples, SlideChat is trained to learn the associations between WSIs and diagnostic reports and interpret complex queries in clinical practice. 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.

Indexed as

Artificial IntelligenceImage Interpretation, Computer-AssistedNeoplasmsGenerative Artificial IntelligenceHumansLarge Language Models

Identifiers

PMID42680939

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.