Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities

TL;DR

This survey reviews recent progress in explainable AI for clinical risk prediction, emphasizing interpretability, fairness, validation, and the importance of transparency and stakeholder involvement for trustworthy healthcare AI systems.

Contribution

It provides a comprehensive overview of concepts, methods, and modalities in explainable AI for clinical risk prediction, highlighting evaluation, validation, and open resources.

Findings

Progress in developing explainable models for clinical risk prediction

Importance of external validation and diverse interpretability methods

Need for transparency, reproducibility, and stakeholder involvement

Abstract

Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexity of AI models, however, concerns regarding their opacity, potential biases, and the need for interpretability. To ensure trust and reliability in AI systems, especially in clinical risk prediction models, explainability becomes crucial. Explainability is usually referred to as an AI system's ability to provide a robust interpretation of its decision-making logic or the decisions themselves to human stakeholders. In clinical risk prediction, other aspects of explainability like fairness, bias, trust, and transparency also represent important concepts beyond just interpretability. In this review, we address the relationship between these concepts as they are often used together or interchangeably. This review also discusses recent progress in developing explainable models for clinical risk prediction, highlighting the importance of quantitative and clinical evaluation and validation across multiple common modalities in clinical practice. It emphasizes the need for external validation and the combination of diverse interpretability methods to enhance trust and fairness. Adopting rigorous testing, such as using synthetic datasets with known generative factors, can further improve the reliability of explainability methods. Open access and code-sharing resources are essential for transparency and reproducibility, enabling the growth and trustworthiness of explainable research. While challenges exist, an end-to-end approach to explainability in clinical risk prediction, incorporating stakeholders from clinicians to developers, is essential for success.