KRAL: Knowledge and Reasoning Augmented Learning for LLM-assisted Clinical Antimicrobial Therapy

Clinical antimicrobial therapy requires the dynamic integration of pathogen profiles, host factors, pharmacological properties of antimicrobials, and the severity of infection.This complexity imposes fundamental limitations on the applicability of Large Language Models (LLMs) in high-stakes clinical decision-making including knowledge gaps, data privacy concerns, high deployment costs, and limited reasoning capabilities. This is the first author footnote. A hierarchical evaluation employing diverse teacher-model proxies reduces assessment costs, while modular interface design facilitates seamless system updates. Experimental results demonstrate that KRAL significantly outperforms traditional Retrieval-Augmented Generation (RAG) and Supervised Fine-Tuning (SFT) methods. It improves knowledge question-answering capability (Accuracy@1 on the external open-source benchmark MEDQA increased by 1.8% vs. SFT and 3.6% vs. RAG) and reasoning capability (Pass@1 on the external benchmark PUMCH Antimicrobial increased by 27% vs. SFT and 27.2% vs. RAG), achieved at 20% of SFT's long-term training costs. This establishes KRAL as an effective solution for enhancing local LLMs' clinical diagnostic capabilities, enabling low-cost, high-safety deployment in complex medical decision support. Introduction Antimicrobial therapy constitutes a cornerstone of modern clinical practice. The formulation of an effective regimen necessitates the integration of pathogen-specific factors, host characteristics, pharmacokinetic pharma-codynamic (PK/PD) properties of antimicrobials, and infection severity, all of which are dynamic and interrelated. This places significant cognitive load on clinicians, especially for non-infectious disease specialists or in situations where pathogens are unknown and time is limited, which may result in sub-optimal prescribing decisions, thereby increasing the likelihood of therapeutic failure, antimicrobial toxicity, and the emergence of multidrug-resistant (MDR) pathogens. Large language models (LLMs) have recently emerged as promising tools for enhancing clinical decision support systems (CDSS), owing to their advanced natural language understanding and generation capabilities. Nevertheless, the direct deployment of general-purpose LLMs in high-stakes clinical domains such as antimicrobial therapy is fraught with limitations, including: Knowledge bias: Medical content constitutes <0.3 % of the pre-training corpora[1] in mainstream LLMs (e.g., GPT-3), resulting in limited coverage of rare or emerging pathogens[2-4], outdated guideline adherence[5-9](Appendix A1), and suboptimal performance on atypical presentations. 2 Data privacy and compliance risks: The use of closed-source, cloud-based LLMs (e.g., GPT-4) for processing unencrypted protected health information (PHI) may violate HIPAA/GDPR-equivalent regulations, even under private deployment scenarios if online guideline updates are required[10-12].