AI-Assisted Clinical Decision Support via Efficient Language Model Reasoning
Keywords:
clinical decision support; large language models; efficient reasoning; deployment infrastructure; governance; fairnessAbstract
Clinical decision support is undergoing a transformation driven by large language models capable of flexible medical reasoning. Yet the adoption of these models in safety-critical health care environments remains constrained by computational cost, latency, data governance, robustness, and fairness. This paper presents a system-level analysis of AI-assisted clinical decision support through efficient language model reasoning. It examines the structural trade-offs among model scale, reasoning depth, inference cost, and clinical reliability. The discussion situates efficient reasoning within the broader socio-technical infrastructure of modern health care, emphasizing that benchmark performance alone is insufficient for deployment. The paper analyzes architectural approaches including reasoning path filtering, parameter-efficient adaptation, distillation, and chain-of-thought methods, while considering their implications for auditability, interoperability, and institutional governance. It further addresses fairness and bias as systemic properties that arise from data provenance, workflow integration, and policy design rather than from isolated model components. Sustainability and regulatory considerations are integrated into the analysis, with attention to lifecycle costs, model facts transparency, and evolving artificial intelligence policy. The paper argues that clinically effective language model reasoning requires a layered governance architecture in which efficient inference is coupled with verification, human oversight, and continuous post-deployment monitoring. The conclusion outlines priorities for health systems, developers, and regulators seeking to balance innovation with patient safety.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
