Democratizing cost-effective, agentic artificial intelligence to multilingual medical summarization through knowledge distillation.

ABSTRACT: The increasing demand for multilingual capabilities in healthcare technology highlights the critical need for AI solutions capable of handling underrepresented languages, such as Arabic, in clinical documentation. Arabic's unique linguistic complexities-morphological richness, syntactic variations, and diglossia-present significant challenges for foundational large language models (LLMs), especially in domain-specific tasks like medical summarization. This study introduces AraSum, a domain-specific AI agent built using a novel knowledge distillation framework that transforms large multilingual LLMs into lightweight, task-optimized small language models (SLMs). Leveraging a synthetic dataset of Arabic medical dialogues, AraSum demonstrates superior performance over JAIS-30B, a foundational

SUBMITTER: Lee C 

PROVIDER: S-EPMC12307869 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature