Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges.

Area of Science:

• Natural Language Processing
• Clinical Informatics
• Artificial Intelligence in Medicine

Background:

• Large language models (LLMs) are increasingly utilized for analyzing clinical narratives within electronic health records (EHRs).
• LLMs offer potential for enhancing documentation efficiency and extracting secondary data from clinical notes.
• The rapid advancement of LLMs necessitates a synthesis of current evidence regarding their application in healthcare.

Purpose of the Study:

• To synthesize recent evidence on the methodological approaches and applications of LLMs for clinical narrative processing.
• To assess the performance, benefits, limitations, and implications of LLMs in clinical practice.
• To provide a high-level overview of emerging LLM applications in clinical settings.

Main Methods:

• Non-systematic synthesis of representative studies published between 2022 and 2026.
• Focused on methodological approaches and diverse applications of LLMs in clinical narrative analysis.
• Evaluated performance metrics, identified benefits, and documented limitations.

Main Results:

• LLMs demonstrated strong performance in information extraction, summarization, triage prediction, section classification, and synthetic text generation.
• LLMs often surpassed traditional machine-learning models in these tasks.
• Improvements were noted in converting unstructured notes to actionable insights, reducing documentation burden, and supporting decision-making.

Conclusions:

• LLMs hold substantial promise for transforming clinical narrative processing and improving healthcare workflows.
• Key challenges include model "hallucinations," reproducibility issues, prompt sensitivity, domain adaptation, and transparency limitations.
• Safe adoption of LLMs in clinical practice requires rigorous evaluation, continuous auditing, and addressing identified limitations.