Citation

Sreejith, Reshma and Ramasamy, R. Kanesaraj and Mohd Isa, Wan Noorshahida and Abdullah, Junaidi (2026) Enhancing Respiratory Disease Diagnosis: Evaluating the Efficiency and Generalizability of AI-Driven Lung Sound Analysis Models. Lecture Notes in Networks and Systems, 1525. pp. 449-465. ISSN 2367-3370

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Abstract

The incorporation of artificial intelligence (AI) in health care has created new opportunities for enhancing diagnostic precision, especially in the identification of respiratory diseases. This work examines the computational efficiency and generalizability of AI-driven lung sound analysis models, focusing on their performance across varied patient populations and datasets. The scalability and resilience of these models are rigorously assessed in practical clinical environments, addressing challenges such as demographic variability, data integrity, and environmental interference. Emphasis is placed on maintaining an essential balance between achieving high-diagnostic accuracy and maximizing computational efficiency, illustrating how effective models can enhance clinical processes and facilitate swift, precise decision-making across various healthcare settings. The findings underscore the potential of AI-driven lung sound analysis to revolutionize respiratory care, offering insights that can improve model adaptation and ensure equitable healthcare delivery among diverse patient populations.

Item Type:	Article
Uncontrolled Keywords:	AI-based diagnosis, generalizability of AI models
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RA Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	10 Feb 2026 03:20
Last Modified:	10 Feb 2026 03:20
URII:	http://shdl.mmu.edu.my/id/eprint/15282

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