{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T10:14:39Z","timestamp":1788257679960,"version":"build-2803163510"},"reference-count":18,"publisher":"The Korean Society of Medical Informatics","issue":"2","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"New Frontier Research Fund","award":["NFRFE-201901365"],"award-info":[{"award-number":["NFRFE-201901365"]}]}],"content-domain":{"domain":["e-hir.org"],"crossmark-restriction":true},"short-container-title":["Healthc Inform Res"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>Objectives: This study proposes a mobile-based explainable artificial intelligence (XAI) platform designed for diagnosing febrile illnesses.Methods: We integrated the interpretability offered by local interpretable model-agnostic explanations (LIME) and the explainability provided by generative pre-trained transformers (GPT) to bridge the gap in understanding and trust often created by machine learning models in critical healthcare decision-making. The developed system employed random forest for disease diagnosis, LIME for interpretation of the results, and GPT-3.5 for generating explanations in easy-to-understand language.Results: Our model demonstrated robust performance in detecting malaria, achieving precision, recall, and F1-scores of 85%, 91%, and 88%, respectively. It performed moderately well in detecting urinary tract and respiratory tract infections, with precision, recall, and F1-scores of 80%, 65%, and 72%, and 77%, 68%, and 72%, respectively, maintaining an effective balance between sensitivity and specificity. However, the model exhibited limitations in detecting typhoid fever and human immunodeficiency virus\/acquired immune deficiency syndrome, achieving lower precision, recall, and F1-scores of 69%, 53%, and 60%, and 75%, 39%, and 51%, respectively. These results indicate missed true-positive cases, necessitating further model fine-tuning. LIME and GPT-3.5 were integrated to enhance transparency and provide natural language explanations, thereby aiding decision-making and improving user comprehension of the diagnoses.Conclusions: The LIME plots revealed key symptoms influencing the diagnoses, with bitter taste in the mouth and fever showing the highest negative influence on predictions, and GPT-3.5 provided natural language explanations that increased the reliability and trustworthiness of the system, promoting improved patient outcomes and reducing the healthcare burden.<\/jats:p>","DOI":"10.4258\/hir.2025.31.2.125","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T03:02:02Z","timestamp":1746673322000},"page":"125-135","update-policy":"https:\/\/doi.org\/10.4258\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Developing an Explainable Artificial Intelligence System for the Mobile-Based Diagnosis of Febrile Diseases Using Random Forest, LIME, and 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