Qualitative Framework for Evaluating Clinical Data Science Systems: Beyond Technical Validation

I. IntroductionThe proliferation of data science and artificial intelligence (AI) in healthcare has led to the rapid deployment of predictive models and clinical decision-support tools [1-3].Although advanced technical performance metrics are now available, real-world failures of healthcare AI systems often do not arise from algorithmic weaknesses alone [4,5].Instead, such failures are commonly driven by limitations in how these systems are evaluated and implemented, particularly with respect to system safety, clinical workflow integration, and human-AI interactions [6][7][8].Increasing evidence suggests that failures in clinical AI result not only from algorithm design, but also from inadequate evaluation, governance, and implementation processes [4,9].This communication presents a conceptual framework for evaluating clinical data science systems in healthcare.It draws on established evaluation frameworks in health infor-matics as well as the author's direct experience implementing and evaluating clinical data science systems in healthcare settings. II. CARES Framework: A Qualitative Approach to System EvaluationThe CARES framework (Clinical relevance, Appropriateness of data, Reliability and safety, Engagement and usability, and Sustainability and governance) is a conceptual evaluation framework (Figure 1) that addresses five key limitations in current approaches to evaluating clinical data science systems