AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology

Digital pathology and artificial intelligence (AI) hold immense transformative potential to revolutionize cancer diagnostics, treatment outcomes, and biomarker discovery. Gaining a deeper understanding of deep learning algorithm methods applied to histopathological data and evaluating their performance on different tasks is crucial for developing the next generation of AI technologies. To this end, we developed AI in Histopathology Explorer (HistoPathExplorer); an interactive dashboard with intelligent tools available at www.histopathexpo.ai. This real-time online resource enables users, including researchers, decision-makers, and various stakeholders, to assess the current landscape of AI applications for specific clinical tasks, analyze their performance, and explore the factors influencing their translation into practice. Moreover, a quality index was defined to evaluate the comprehensiveness of methodological details in published AI methods. HistoPathExplorer highlights opportunities and challenges for AI in histopathology, and offers a valuable resource for creating more effective methods and shaping strategies and guidelines for translating digital pathology applications into clinical practice.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-07
DOI
https://doi.org/10.5281/zenodo.16759075
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
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article

AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology

Zenodo (CERN European Organization for Nuclear Research)
AI in cancer detection
article

AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology

article en

Abstract

Digital pathology and artificial intelligence (AI) hold immense transformative potential to revolutionize cancer diagnostics, treatment outcomes, and biomarker discovery. Gaining a deeper understanding of deep learning algorithm methods applied to histopathological data and evaluating their performance on different tasks is crucial for developing the next generation of AI technologies. To this end, we developed AI in Histopathology Explorer (HistoPathExplorer); an interactive dashboard with intelligent tools available at www.histopathexpo.ai. This real-time online resource enables users, including researchers, decision-makers, and various stakeholders, to assess the current landscape of AI applications for specific clinical tasks, analyze their performance, and explore the factors influencing their translation into practice. Moreover, a quality index was defined to evaluate the comprehensiveness of methodological details in published AI methods. HistoPathExplorer highlights opportunities and challenges for AI in histopathology, and offers a valuable resource for creating more effective methods and shaping strategies and guidelines for translating digital pathology applications into clinical practice.

Zenodo (CERN European Organization for Nuclear Research)
King's College London (GB)
Openalex Percentile: Top 74%
AI in cancer detection
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AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS