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.

翻译：近年来，人工智能在医疗领域的应用展现出超越人类在诊断和疾病预后方面的惊人潜力。然而，随着AI模型日趋复杂，其黑箱性、潜在偏差及对可解释性的需求引发关注。为确保AI系统（尤其是临床风险预测模型）的可靠性与可信度，可解释性至关重要。可解释性通常指AI系统向人类利益相关者提供其决策逻辑或决策本身稳健解释的能力。在临床风险预测中，公平性、偏差、可信度与透明度等可解释性的其他维度，同样是超越单纯可解释性的重要概念。本综述梳理了这些常被混用或交替使用的概念之间的关联，同时探讨了开发面向临床风险预测的可解释模型的最新进展，强调在临床实践多种常见模态中进行定量评估与临床评估验证的重要性。研究指出，采用外部验证、融合多样化可解释性方法有助于增强可信度与公平性。通过采用已知生成因子的合成数据集等严格测试手段，可进一步提升可解释性方法的可靠性。开放获取与代码共享资源对确保透明度与可重复性至关重要，能够促进可解释性研究的健康发展与可信度提升。尽管存在挑战，但通过整合从临床医生到开发人员等利益相关者的端到端可解释性方法，是临床风险预测领域取得成功的关键。