Large Language Models for Explainable Medical Text Summarization: A Systematic Literature Review

Aleka Melese Ayalew; Md Rabiul Hasan; Tapio Seppänen; Mourad Oussalah;

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ABSTRACT Recently, large language models have gained momentum in several areas, including medical text summarization and clinical decision support in healthcare. In this systematic review, we focus on how LLMs are used for summarization and explainability, and on how they improve the precision of clinical decisions.

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ABSTRACT

Recently, large language models have gained momentum in several areas, including medical text summarization and clinical decision support in healthcare. In this systematic review, we focus on how LLMs are used for summarization and explainability, and on how they improve the precision of clinical decisions. Initially, by screening eight distinct databases, we identified 1601 studies using the PRISMA protocol. Following strict qualifying criteria, 61 studies were selected for the final review. Our results show that decoder‐only architectures dominate the field (60.7% of studies), with zero‐shot prompting emerging as the predominant approach (55.7%), while 24.6% of studies employed fine‐tuning. General clinical decision support (26.2%) and diagnostic assistance (19.7%) constitute the two most widely used clinical applications across multiple clinical specialties. Identified in 26.2% of studies, the intersection of summarization and explainability becomes a key focus area, with fine‐tuned models obtaining 38.6% higher average ROUGE‐1 scores compared to zero‐shot approaches, and studies including robust explainability features report 27.3% higher clinician acceptance rates. Despite great potential, major obstacles still exist, including model hallucinations (63.9% of studies), minimal workflow integration (only 14.8% of studies), and inadequate attention to regulatory paths (8.2%). Although LLMs offer significant potential for transforming clinical decision support via improved information handling and clear reasoning, realizing these goals requires overcoming essential challenges in clinical validation, system integration, and ethical oversight. This review aims to provide an extensive framework for understanding the current capabilities, limitations, and prospects of LLM‐based explainable summarization in clinical settings, guiding future research and practical deployment. This article is categorized under: Technologies > Artificial Intelligence Fundamental Concepts of Data and Knowledge > Explainable AI Application Areas > Health Care

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