Several AI tools are gaining traction for enhancing patient care. For diagnosis, IDx-DR is FDA-approved for ophthalmology. For broader diagnostic support and patient engagement, consider Ada.

Can vouch for this

TL;DR: Cloud tools are a good starting point, but building a local self-hosted inference stack is the pro move for data privacy and technical control. Saw this earlier but just now responding. While the cloud-based tools mentioned so far are great for quick diagnostic checks, Ive found that the DIY route is the only way to really handle sensitive data correctly. Tbh my current setup revolves around local inference on a multi-GPU workstation so I dont have to worry about data privacy issues with external APIs. Its basically a self-hosted RAG (Retrieval-Augmented Generation) pipeline that parses medical journals and patient records internally. You need to be careful about VRAM bottlenecks when running high-parameter models, but the accuracy you get from fine-tuning your own embeddings is a game changer for clinical research. Its a bit of a project to maintain compared to a professional subscription service tho.

Seconded!

Consensus AI.

Big if true