Structural insights into clinical large language models and their barriers to translational readiness.

Abstract

Despite rapid integration into clinical decision-making, clinical large language models (LLMs) face substantial translational barriers due to insufficient structural characterization and limited external validation. We systematically map the clinical LLM research landscape to identify key structural patterns influencing their readiness for real-world clinical deployment. We identified 73 clinical LLM studies published between January 2020 and March 2025 using a structured evidence-mapping approach. To ensure transparency and reproducibility in study selection, we followed key principles from the PRISMA 2020 framework. Each study was categorized by clinical task, base architecture, alignment strategy, data type, language, study design, validation methods, and evaluation metrics. Studies often addressed multiple early stage clinical tasks-question answering (56.2%), knowledge structuring (31.5%), and disease prediction (43.8%)-primarily using text data (52.1%) and English-language resources (80.8%). GPT models favored retrieval-augmented generation (43.8%), and LLaMA models consistently adopted multistage pretraining and fine-tuning strategies. Only 6.9% of studies included external validation, and prospective designs were observed in just 4.1% of cases, reflecting significant gaps in translational reliability. Evaluations were predominantly quantitative only (79.5%), though qualitative and mixed-method approaches are increasingly recognized for assessing clinical usability and trustworthiness. Clinical LLM research remains exploratory, marked by limited generalizability across languages, data types, and clinical environments. To bridge this gap, future studies must prioritize multilingual and multimodal training, prospective study designs with rigorous external validation, and hybrid evaluation frameworks combining quantitative performance with qualitative clinical usability metrics.