Beyond the Benchmark: What It Takes for Medical AI to Work in Practice
Medical AI has made remarkable progress on benchmarks, but strong model performance alone does not guarantee clinical value. This talk will examine the shift from narrow prediction models toward systems that integrate imaging and clinical context, reason across complex tasks, and support clinicians in taking action. Drawing on lessons from medical imaging research and translation, it will outline how realistic evaluation, workflow-aware design, and thoughtful clinician–AI collaboration can help turn technical capability into reliable and useful clinical systems.
Objectives
Explain why performance on conventional benchmarks may not predict an AI system’s reliability or usefulness in clinical practice.
Identify the roles of multimodal context, clinical reasoning, and workflow integration in building clinically useful AI systems.
Apply a framework for evaluating medical AI across technical performance, clinician–AI interaction, and clinical outcomes.
Speakers
Pranav Rajpurkar, PhD
Associate Professor, Biomedical Informatics, Harvard University
Co-founder, a2z Radiology AI