Artificial Intelligence-Driven Clinical Decision Support Systems

TL;DR

The chapter analyzes AI-driven clinical decision support systems, emphasizing the need for rigorous validation, calibration, clinical usefulness, and clinical impact assessment. It integrates fairness, explainability, and privacy-preservation into the development lifecycle, outlining practical methods such as external validation, calibration curves, decision curve analysis, and privacy-preserving strategies like differential privacy and federated learning. The work provides concrete guidance and examples, including external validation in psychosis risk, to illustrate generalizability and real-world utility, while warning about privacy attacks and explanation manipulation. It advocates a holistic, human-centered approach and ongoing collaboration to deploy trustworthy AI in healthcare that improves patient outcomes while protecting data privacy and equity.

Abstract

As artificial intelligence (AI) becomes increasingly embedded in healthcare delivery, this chapter explores the critical aspects of developing reliable and ethical Clinical Decision Support Systems (CDSS). Beginning with the fundamental transition from traditional statistical models to sophisticated machine learning approaches, this work examines rigorous validation strategies and performance assessment methods, including the crucial role of model calibration and decision curve analysis. The chapter emphasizes that creating trustworthy AI systems in healthcare requires more than just technical accuracy; it demands careful consideration of fairness, explainability, and privacy. The challenge of ensuring equitable healthcare delivery through AI is stressed, discussing methods to identify and mitigate bias in clinical predictive models. The chapter then delves into explainability as a cornerstone of human-centered CDSS. This focus reflects the understanding that healthcare professionals must not only trust AI recommendations but also comprehend their underlying reasoning. The discussion advances in an analysis of privacy vulnerabilities in medical AI systems, from data leakage in deep learning models to sophisticated attacks against model explanations. The text explores privacy-preservation strategies such as differential privacy and federated learning, while acknowledging the inherent trade-offs between privacy protection and model performance. This progression, from technical validation to ethical considerations, reflects the multifaceted challenges of developing AI systems that can be seamlessly and reliably integrated into daily clinical practice while maintaining the highest standards of patient care and data protection.

Figures (11)

• Figure 1: Model Development
• Figure 2: $k$-fold cross validation
• Figure 3: Performance evaluation of ML models.
• Figure 4: A Nonremission Risk Prediction Model in First-Episode Psychosis (reproduced with permission leighton2021development)
• Figure 5: Probability calibration plot (reproduced with permission leighton2021development)