Healthcare AI

Healthcare AI refers to machine learning models applied to medical data for tasks like diagnosis, treatment planning, drug discovery, and patient monitoring. In local AI, operators run models such as Med-PaLM 2, BioBERT, or fine-tuned Llama variants on clinical notes or medical imaging. These models require careful handling of sensitive data (HIPAA compliance) and often demand high VRAM for large context windows (e.g., 32K tokens for full patient records). Quantization (Q4 or Q8) is common to fit models on consumer GPUs, but accuracy trade-offs must be validated against medical benchmarks.

A radiologist runs a local Llama 3.1 8B model fine-tuned on chest X-ray reports via Ollama on an RTX 4090 (24 GB VRAM). The model processes a 4K-token report in ~2 seconds at Q4_K_M quantization. Without quantization, the same model would exceed VRAM and offload to system RAM, dropping speed to ~10 tok/s.

In LM Studio, an operator loads a quantized BioBERT model for named entity recognition on clinical notes. They set context length to 8K tokens to cover a full patient history. The model outputs structured diagnoses and medications. For HIPAA compliance, all data stays local—no API calls. If VRAM is tight (e.g., 8 GB), they switch to Q4 quantization via llama.cpp's -b 4 flag.