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Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models 期刊论文

编号：

3722B412352CC8D0C5CE0B6F555B35E8
作者：

Attai, Kingsley^[1] Ekpenyong, Moses^[2,3,4] Amannah, Constance^[5] Asuquo, Daniel^[6] Ajuga, Peterben^[7] Obot, Okure^[2] Johnson, Ekemini^[1] John, Anietie^[1] Maduka, Omosivie^[8] Akwaowo, Christie^[9] Uzoka, FaithMichael^[10]
语种：

英文
期刊：

TROPICAL MEDICINE AND INFECTIOUS DISEASE ISSN：2414-6366 2024 年 9 卷 9 期 ; SEP
疾病分类：

伤寒和副伤寒、疟疾
关键词：

malaria diagnosis typhoid diagnosis machine learning XAI LIME GPT BERT ChatGPT Gemini perplexity explainability interpretability
摘要：

Malaria and Typhoid fever are prevalent diseases in tropical regions, and both are exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of similar symptoms. Although machine learning (ML) models offer accurate predictions, they operate as "black boxes" with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like GPT to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics by describing which symptoms had the greatest impact on the model''s decisions and providing clear, understandable explanations. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed the other tested models; in addition, important features were identified with the LIME plots while ChatGPT 3.5 had a comparative advantage over other LLMs. The study integrates RF, LIME, and GPT in building a mobile app to enhance the interpretability and transparency in malaria and typhoid diagnosis system. Despite its promising results, the system''s performance is constrained by the quality of the dataset. Additionally, while LIME and GPT improve transparency, they may introduce complexities in real-time deployment due to computational demands and the need for internet service to maintain relevance and accuracy. The findings suggest that AI-driven diagnostic systems can significantly enhance healthcare delivery in environments with limited resources, and future works can explore the applicability of this framework to other medical conditions and datasets.
推荐引用方式
GB/T 7714：

Attai Kingsley,Ekpenyong Moses,Amannah Constance, et al. Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models [J].TROPICAL MEDICINE AND INFECTIOUS DISEASE,2024,9(9).
APA：

Attai Kingsley,Ekpenyong Moses,Amannah Constance,Asuquo Daniel,&Uzoka Faith-Michael.(2024).Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models .TROPICAL MEDICINE AND INFECTIOUS DISEASE,9(9).
MLA：

Attai Kingsley, et al. "Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models" .TROPICAL MEDICINE AND INFECTIOUS DISEASE 9,9(2024).
数据来源自科睿唯安Web of Science核心合集
入库时间：

2024/10/24 21:07:49
更新时间：

2024/10/24 21:07:49

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