Explainable AI for clinical risk prediction: a survey of concepts,
  methods, and modalities
Munib Mesinovic Peter Watkinson Tingting Zhu
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.