Human-in-the-Loop as a Safety Guardrail: Clinical Accountability in the Large Language Model Era

UNSTRUCTURED We talk about Zhang et al.'s review of LLMs in healthcare and stress that how well they work in practice is what matters most. There are still some big problems: 2–10 second latencies, a lot of VRAM needed, and the ability to handle many users at once. We suggest standard metrics (operations per diagnosis, energy per inference, cost-effectiveness) and ways to improve performance, such as quantization/pruning, edge deployment, and hybrid architectures. In an initial benchmarking, approximately 14 billion-parameter medical models attained 85–90% of GPT-4’s accuracy utilizing roughly 15% of the resources. We think that HPC and biomedical informatics should be aligned for fair and efficient use.