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.