Session Introduction: AI and Machine Learning in Clinical Medicine Bridging or Separating Model Intelligence and Human Expertise

Abstract

Artificial Intelligence (AI) technologies continue to expand their role in clinical medicine, with large language models (LLMs) and multimodal systems now applied to communication, imaging, and predictive analytics. Advances in generative and retrieval-augmented methods have improved the accuracy and contextual grounding of clinical summaries, patient messaging, and decision support. At the same time, new benchmarks in imaging, vision, and spontaneous speech have underscored both progress and the persistence of unsolved challenges. Predictive modeling efforts highlight causality, longitudinal trajectories, and informative clinical events, while methodological contributions emphasize uncertainty management, abstention, and interpretable causal structures. Finally, frameworks for evaluation and governance address the crucial gap between laboratory performance and real-world deployment.

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