A Brief Review of Explainable Artificial Intelligence in Healthcare

TL;DR

This paper systematically reviews explainable AI techniques in healthcare, emphasizing their importance for transparency, trust, and safety in high-stakes medical decision-making.

Contribution

It categorizes XAI methods in healthcare, discusses their challenges, and highlights the role of explainability in safety-critical medical applications.

Findings

XAI methods are categorized into six groups.

Explainability enhances trust among clinicians.

Identifies research gaps and limitations in current XAI approaches.

Abstract

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated.