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Explainable AI in Computational Pathology: A New Framework Proposed

By PulseAugur Editorial · [1 sources] ·

A new review paper published on arXiv addresses the fragmentation in explainable AI (XAI) research within computational pathology. The paper proposes a standardized vocabulary, a taxonomy of XAI methods, and a framework to map clinical questions to recommended XAI approaches. It identifies key gaps hindering clinical adoption and suggests actionable steps for advancing XAI in this medical field. AI

IMPACT Standardizes XAI terminology and methods, potentially accelerating safe AI adoption in clinical pathology.

RANK_REASON The cluster contains an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Explainable AI in Computational Pathology: A New Framework Proposed

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The cluster contains an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shubham Innani, Suhang You, Adam Shephard, Bhakti Baheti, Francesco Ciompi, Joe Yeong, Nasir Rajpoot, Michael Feldman, Solene Florence Kammerer-Jacquet, Dimitrios Makris, Geert Litjens, Anne L. Martel, Jana Lipkova, April Khademi, Spyridon Bakas, for the… ·

    Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations

    arXiv:2608.28820v1 Announce Type: new Abstract: Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and treatment prediction from gigapixel whole-slide images. Clinical adoption is prog…

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