Technical deep-dives on the hard problems in clinical AI data — annotation, evaluation, and model alignment.
Where the real bottlenecks are in physician-led annotation for clinical AI — from reasoning traces to schema design to the disagreement problem.
Why most clinical AI benchmarks are broken — and what it takes to build evaluation datasets that actually measure clinical reasoning.
How physician preference data drives RLHF and DPO pipelines for clinical LLMs — and why alignment is an ongoing requirement, not a one-time task.
What actually works, what doesn't, and why data quality matters more than model architecture in clinical AI.