Human-in-the-Loop as a Safety Guardrail: Clinical Accountability in the LLM Era (Preprint)

Abstract

<sec> <title>UNSTRUCTURED</title> 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. </sec>

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