Exploring Radiologists' Expectations of Explainable Machine Learning Models in Medical Image Analysis

TL;DR

This paper investigates radiologists' expectations for explainable machine learning models in medical imaging, proposing guidelines to enhance clinical acceptance and integration.

Contribution

It provides a systematic collection of radiologists' insights and guidelines for designing explainable ML models tailored for radiology practice.

Findings

Radiologists identify key clinical tasks where ML can be most beneficial.

Guidelines are proposed to improve explainability and clinical validation of ML models.

Insights from radiologists can guide development of more acceptable ML tools.

Abstract

In spite of the strong performance of machine learning (ML) models in radiology, they have not been widely accepted by radiologists, limiting clinical integration. A key reason is the lack of explainability, which ensures that model predictions are understandable and verifiable by clinicians. Several methods and tools have been proposed to improve explainability, but most reflect developers' perspectives and lack systematic clinical validation. In this work, we gathered insights from radiologists with varying experience and specialties into explainable ML requirements through a structured questionnaire. They also highlighted key clinical tasks where ML could be most beneficial and how it might be deployed. Based on their input, we propose guidelines for designing and developing explainable ML models in radiology. These guidelines can help researchers develop clinically useful models, facilitating integration into radiology practice as a supportive tool.