State of AI in Healthcare

Design and evaluate biomolecular models to predict protein structures, generate and screen candidate molecules, dock ligands, and estimate key properties to prioritize hits and optimize leads across therapeutic programs.

Run secondary analysis pipelines that turn raw sequencing reads into high‑quality variants, accelerating alignment, processing, and germline/somatic calling for whole genomes and exomes across short‑ and long‑read data.

Build multimodal AI agents that automate patient engagement, summarize and structure clinical documentation, and surface insights from electronic health records (EHR)s and real‑world data to support clinical development and operations.

Build real‑time systems that ingest streaming signals from imaging and other sensors, apply AI to enhance visualization and decision support during procedures, and deliver results with low latency at the point of care.

Accelerate imaging workflows by building, training, and deploying AI models with open‑source tools for tasks like segmentation, classification, registration, and multimodal analysis that combine CT, MR, pathology, endoscopy, and clinical text.