New State-of-the-art Accuracy for the 3 Primary Uses of Healthcare Language Models

This talk presents new levels of accuracy that have very recently been achieved, on public and independently reproducible benchmarks, on the three most common use cases for language models in healthcare:

• Understanding clinical documents: Such as information extraction from clinical notes and reports; detecting entities, relationships, and medical codes; de-identification; and summarization.
• Reasoning about patients: Fusing information across multiple modalities (tabular data, free text, imaging, omics) to create a longitudinal view of each patient, including making reasonable inferences and explaining them.
• Answering medical questions: Answering medical licensing exam questions, biomedical research questions, and similar medical knowledge questions – accurately, without hallucinations, and while citing relevant sources.

Join to learn what has recently become possible in the fast-changing world of Healthcare AI.