Advancing Complex Medical Communication in Arabic with Sporo AraSum: Surpassing Existing Large Language Models

TL;DR

This study introduces Sporo AraSum, a specialized Arabic medical language model that significantly outperforms existing models in clinical summarization, addressing linguistic challenges and enhancing healthcare communication.

Contribution

Sporo AraSum is a novel Arabic medical NLP model tailored for clinical documentation, surpassing existing models in accuracy and cultural competence.

Findings

Sporo AraSum outperforms JAIS in quantitative metrics.

Sporo AraSum demonstrates superior qualitative performance.

The model effectively captures linguistic and cultural nuances.

Abstract

The increasing demand for multilingual capabilities in healthcare underscores the need for AI models adept at processing diverse languages, particularly in clinical documentation and decision-making. Arabic, with its complex morphology, syntax, and diglossia, poses unique challenges for natural language processing (NLP) in medical contexts. This case study evaluates Sporo AraSum, a language model tailored for Arabic clinical documentation, against JAIS, the leading Arabic NLP model. Using synthetic datasets and modified PDQI-9 metrics modified ourselves for the purposes of assessing model performances in a different language. The study assessed the models' performance in summarizing patient-physician interactions, focusing on accuracy, comprehensiveness, clinical utility, and linguistic-cultural competence. Results indicate that Sporo AraSum significantly outperforms JAIS in AI-centric quantitative metrics and all qualitative attributes measured in our modified version of the PDQI-9. AraSum's architecture enables precise and culturally sensitive documentation, addressing the linguistic nuances of Arabic while mitigating risks of AI hallucinations. These findings suggest that Sporo AraSum is better suited to meet the demands of Arabic-speaking healthcare environments, offering a transformative solution for multilingual clinical workflows. Future research should incorporate real-world data to further validate these findings and explore broader integration into healthcare systems.