Large language models in clinical and healthcare scenarios: a global informatics analysis

Abstract

Large language models (LLMs) are gradually evolving from text generators into cognitive interfaces and decision-making infrastructure that can be integrated into clinical systems. Although LLMs have demonstrated tremendous potential in retrieving evidence, structuring medical records, and facilitating clinician-patient communication, their clinical translation still faces systemic challenges in the highly heterogeneous, high-risk, and strictly regulated field of healthcare. This paper aims to utilize bibliometric methods to analyze the current global research landscape and trends regarding LLMs in clinical and healthcare scenarios. Furthermore, it proposes a systematic framework for future research directions, with the goal of providing more actionable guidance for subsequent research and clinical translation pathways in this field. Combining the results of global informatics analysis with relevant research advances, this paper conducts a comprehensive analysis of evaluation methods, deployment processes, and governance strategies for LLMs in the healthcare field, focusing on three key issues: model version drift, multilingual external validation, and prompt injection security governance. Future research on LLMs in the healthcare field should shift its focus from merely demonstrating model performance to building a closed-loop system of clinical evidence and governance that is reproducible, monitorable, auditable, and explainable. Through model drift governance, multilingual external validation, and system-level security governance, it is possible to enhance the models’ long-term reliability, generalization fairness, and the controllability and accountability of their deployment, respectively. On this basis, LLM applications may be cautiously expanded from low-risk, reviewable tasks to medical education, clinical decision support, and real-world healthcare workflows.