L. Pawlik

Enhancing clinical reasoning and diagnostic precision through scaling laws and multi-stage supervised fine-tuning in open-weight medical large language models

A robust technical framework is established for developing trustworthy, high-efficiency medical AI systems capable of operating entirely within hospital-controlled infrastructure by evaluating the synergistic impact of context window scaling and multi-stage supervised fine-tuning within a localized Retrieval-Augmented Generation framework.