Which Clinical AI Training Platform Should You Use?
Start here: what are you actually trying to do?
Your choice of clinical AI training platform depends on your specific use case. This decision guide walks through the critical questions.
"Rapha Protocol. Full stop. You're training on one institution's data. You don't need multi-site FL coordination. You need: hardware-enforced isolation so the hospital trusts the setup, kernel air-gap so no data leaks during training, OPA policy so the hospital sets the rules, and per-job settlement so you pay for what you use. No other platform provides all four as integrated infrastructure."
"Consider your options carefully. FL platforms (NVIDIA FLARE, Rhino Health) can coordinate multi-site training but introduce gradient leakage risk and non-IID convergence problems. Rapha can deploy edge appliances at each site — each training independently, no coordination required, no gradient transmission. You get the same result (model trained on multi-site data) without FL's coordination overhead and privacy risks. The downside: you need an edge appliance at each site. For 10+ sites, that's a hardware deployment consideration."
"Rapha Protocol. Compute-to-data eliminates data movement — the primary regulatory trigger. Hardware TEE provides independently verifiable isolation. Kernel air-gap proves no network exfiltration during training. On-chain proof receipts provide auditable evidence. No cloud BAA. No gradient transmission. No data sharing agreement. Your DPO and Caldicott Guardian will recognise the difference between 'data stays local' as a marketing claim and 'data stays local because the network is physically severed during training' as an architectural guarantee."