“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.”

Bio:
Dr Brad Segal is a clinician-engineer pursuing a DPhil in Biomedical Engineering at Oxford’s Computational Health Informatics Lab under Professor David Clifton. His research focuses on robust deployment strategies for clinical machine learning systems, with particular emphasis on resource-constrained environments. As a practicing physician in South Africa’s public health sector, he has unique insights into the practical challenges of AI implementation across diverse healthcare settings.Brad’s work spans both technical development and clinical implementation, having co-founded healthcare ventures in predictive analytics and clinical decision support that now serve millions of patients across sub-Saharan Africa. As both a technology developer and clinical end-user, he brings practical perspective to the challenges of transitioning machine learning systems from research environments to clinical practice.