Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP Applications
Recent studies have highlighted the growing integration of Natural Language Processing (NLP) techniques and Large Language Models (LLMs) in healthcare. These technologies have shown promising outcomes across various healthcare tasks, especially in widely studied languages like English and Chinese. W...
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MDPI AG
2024-11-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/8/11/157 |
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| author | Rasheed Mohammad Omer S. Alkhnbashi Mohammad Hammoudeh |
| author_facet | Rasheed Mohammad Omer S. Alkhnbashi Mohammad Hammoudeh |
| author_sort | Rasheed Mohammad |
| collection | DOAJ |
| description | Recent studies have highlighted the growing integration of Natural Language Processing (NLP) techniques and Large Language Models (LLMs) in healthcare. These technologies have shown promising outcomes across various healthcare tasks, especially in widely studied languages like English and Chinese. While NLP methods have been extensively researched, LLM applications in healthcare represent a developing area with significant potential. However, the successful implementation of LLMs in healthcare requires careful review and guidance from human experts to ensure accuracy and reliability. Despite their emerging value, research on NLP and LLM applications for Arabic remains limited particularly when compared to other languages. This gap is largely due to challenges like the lack of suitable training datasets, the diversity of Arabic dialects, and the language’s structural complexity. In this study, a panel of medical experts evaluated responses generated by LLMs, including ChatGPT, for Arabic healthcare inquiries, rating their accuracy between 85% and 90%. After fine tuning ChatGPT with data from the Altibbi platform, accuracy improved to a range of 87% to 92%. This study demonstrates the potential of LLMs in addressing Arabic healthcare queries especially in interpreting questions across dialects. It highlights the value of LLMs in enhancing healthcare communication within the Arabic-speaking world and points to a promising area for further research. This work establishes a foundation for optimizing NLP and LLM technologies to achieve greater linguistic and cultural adaptability in global healthcare settings. |
| format | Article |
| id | doaj-art-e1386ebdee014d0c88cc6de11628fe9a |
| institution | OA Journals |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-e1386ebdee014d0c88cc6de11628fe9a2025-08-20T02:28:10ZengMDPI AGBig Data and Cognitive Computing2504-22892024-11-0181115710.3390/bdcc8110157Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP ApplicationsRasheed Mohammad0Omer S. Alkhnbashi1Mohammad Hammoudeh2College of Computing, Birmingham City University, Birmingham B5 5JU, UKInformation and Computer Science Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi ArabiaInformation and Computer Science Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi ArabiaRecent studies have highlighted the growing integration of Natural Language Processing (NLP) techniques and Large Language Models (LLMs) in healthcare. These technologies have shown promising outcomes across various healthcare tasks, especially in widely studied languages like English and Chinese. While NLP methods have been extensively researched, LLM applications in healthcare represent a developing area with significant potential. However, the successful implementation of LLMs in healthcare requires careful review and guidance from human experts to ensure accuracy and reliability. Despite their emerging value, research on NLP and LLM applications for Arabic remains limited particularly when compared to other languages. This gap is largely due to challenges like the lack of suitable training datasets, the diversity of Arabic dialects, and the language’s structural complexity. In this study, a panel of medical experts evaluated responses generated by LLMs, including ChatGPT, for Arabic healthcare inquiries, rating their accuracy between 85% and 90%. After fine tuning ChatGPT with data from the Altibbi platform, accuracy improved to a range of 87% to 92%. This study demonstrates the potential of LLMs in addressing Arabic healthcare queries especially in interpreting questions across dialects. It highlights the value of LLMs in enhancing healthcare communication within the Arabic-speaking world and points to a promising area for further research. This work establishes a foundation for optimizing NLP and LLM technologies to achieve greater linguistic and cultural adaptability in global healthcare settings.https://www.mdpi.com/2504-2289/8/11/157Large Language ModelNatural Language Processingartificial intelligence in Arabicpatient medical query |
| spellingShingle | Rasheed Mohammad Omer S. Alkhnbashi Mohammad Hammoudeh Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP Applications Big Data and Cognitive Computing Large Language Model Natural Language Processing artificial intelligence in Arabic patient medical query |
| title | Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP Applications |
| title_full | Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP Applications |
| title_fullStr | Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP Applications |
| title_full_unstemmed | Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP Applications |
| title_short | Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP Applications |
| title_sort | optimizing large language models for arabic healthcare communication a focus on patient centered nlp applications |
| topic | Large Language Model Natural Language Processing artificial intelligence in Arabic patient medical query |
| url | https://www.mdpi.com/2504-2289/8/11/157 |
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