Large language models trained on vast datasets could speed genomics research, streamline clinical documentation, improve real-time diagnostics, support clinical decision-making, accelerate drug discovery, and even generate synthetic data to advance experiments.
But their promise to transform biomedical research often runs into a bottleneck: beyond the structured data healthcare relies on, these models struggle in edge cases like rare diseases and unusual conditions, where reliable, representative data is scarce.
