Artificial Intelligence and Machine Learning in Practice

Artificial Intelligence (AI) and machine learning (ML) tools are increasingly marketed for clinical use. Yet many clinicians, researchers, and health system leaders lack structured approaches to critically evaluate these models, understand their real-world implications, and plan for responsible scale. Presented as a partnership between the Clinical Informatics and Digital Health Special Interest Group and the Clinical Research Committee, this session equips learners with practical frameworks to assess AI/ML models; examines the realities of implementation in clinical environments; and explores future-facing strategies for scaling AI while maintaining safety, equity, and trust through the presentation and discussion of real-world examples from clinical research applying AI and ML methodologies.The session concludes with an interactive panel discussion on how to translate AI/ML use from theory into clinical research and practice.  Session organizers aim to equip participants with the knowledge and tools needed to describe appropriate use cases of AI/ML, to understand the implications of employing these approaches, and to think critically about the performance of different AI/ML algorithms in clinical research through a generalized assessment framework.