HistoLens: An Interactive XAI Toolkit for Verifying and Mitigating Flaws in Vision-Language Models for Histopathology

TL;DR

HistoLens is an interactive, transparent AI toolkit that enables pathologists to verify and understand AI decisions in histopathology through natural language questions and visual explanations, enhancing trust and diagnostic confidence.

Contribution

The paper introduces HistoLens, a novel interactive XAI system that translates natural language queries into AI analyses and provides visual proof, improving interpretability and trust in medical AI applications.

Findings

Enables natural language interaction with AI for histopathology.

Provides visual heatmaps for AI decision explanations.

Ensures AI focuses solely on relevant tissue regions.

Abstract

For doctors to truly trust artificial intelligence, it can't be a black box. They need to understand its reasoning, almost as if they were consulting a colleague. We created HistoLens1 to be that transparent, collaborative partner. It allows a pathologist to simply ask a question in plain English about a tissue slide--just as they would ask a trainee. Our system intelligently translates this question into a precise query for its AI engine, which then provides a clear, structured report. But it doesn't stop there. If a doctor ever asks, "Why?", HistoLens can instantly provide a 'visual proof' for any finding--a heatmap that points to the exact cells and regions the AI used for its analysis. We've also ensured the AI focuses only on the patient's tissue, just like a trained pathologist would, by teaching it to ignore distracting background noise. The result is a workflow where the pathologist remains the expert in charge, using a trustworthy AI assistant to verify their insights and make faster, more confident diagnoses.