Legacy ML required a separate model for every data point. GPT-4 now reads unstructured records instantly, beating human accuracy.
A healthcare technology platform bridging the gap between clinical trials and patients by automating the complex screening process for medical providers.
Manual record evaluation created a bottleneck for providers, and a previous machine learning strategy failed to scale because it required building a...
“We deployed fine-tuned state-of-the-art healthcare models, which we then further optimized. You spend a lot of time, and you have to do everything use case by use case. You have to build, train, and validate a separate model for every piece of information.”
Clinical research platform connecting patients, providers, and trial sponsors.
AI research and deployment company developing generative models and tools.
Related implementations across industries and use cases
Reviewing hundreds of pages for 50 criteria caused missed matches. AI now parses records to find eligible patients ahead of visits.
Self-hosting GPUs capped training at weekly runs. A serverless shift cut costs 3x and enabled daily iteration for 87% accuracy.
Diagnostic workups took weeks to compile. AI now analyzes records in 5 minutes, identifying 4x more missing diagnostics.
Manual triage meant urgent faxes sat for days. AI now routes millions of documents to 1,200+ destinations in seconds.
Manual segmentation took 20 minutes, delaying care. AI now analyzes hemorrhages in seconds, aiding decisions across 23 regional sites.
Sonar review meant checking raw footage frame by frame. AI now scans hours of data to pinpoint targets for recovery.
Fragmented server data bottlenecked scaling. A unified model cut hardware SKUs by 50% and eliminated dozens of manual processes.