Beyond the Benchmark: Bridging the Clinical AI Implementation Gap

Despite remarkable advances in machine learning benchmarks for healthcare applications, the gap between research performance and clinical utility remains a critical challenge. Through a series of case studies drawn from implementations across diverse healthcare settings, this talk will analyse how common machine learning practices can lead to unexpected failure modes in clinical environments. We will explore how traditional evaluation metrics can mask critical shortcomings, as well as examining how the misalignment between model optimization objectives and clinical decision-making requirements can compromise real-world implementation.
The discussion will cover fundamental challenges in clinical ML deployment, including the impact of population-specific disease presentations on model generalization, technical constraints of clinical workflow integration, and trade-offs between interpretability and performance. Drawing from experiences implementing systems across various clinical contexts, we will explore potential approaches for identifying and mitigating these challenges, considering both theoretical and practical aspects of building clinical AI systems that better align with real-world healthcare needs.