The Development Process and Lifecycle of Clinical Grade and Other Safety and Performance-Sensitive AI/ML Models

Abstract

AbstractThis chapter introduces the notion of “clinical-grade” and other sensitive, mission-critical models and contrasts such models with more fault-tolerant feasibility, exploratory, or pre-clinical ones. The steps outlined span from requirements engineering to deployment and monitoring and also emphasize a number of contextual factors determining success such as clinical and health economic considerations. AI’s “knowledge cliff” is discussed and the need to operationalize AI/ML “self-awareness” and overcome its limitations to ensure generality and safe use. This chapter introduces many core pitfalls and best practices. The overarching concepts, pitfalls and BPs of the chapter will be elaborated further and implementation will be presented across the book and especially in chapters “Foundations and Properties of AI/ML Systems,” “An Appraisal and Operating Characteristics of Major ML Methods Applicable in Healthcare and Health Science,” “Foundations of Causal ML”, “Model Selection and Evaluation”, and in chapter “Overfitting, Underfitting and General Model Overconfidence and Under-Performance Pitfalls and Best Practices in Machine Learning and AI”.