Abstract

ARTIFICIAL INTELLIGENCE IN PULMONARY DIAGNOSTICS: CURRENT APPLICATIONS AND FUTURE DIRECTIONS: A REVIEW ARTICLE

M. Angel Sowmya*, U. Sowjanya, P. Rashmi and K. Madhuri Sushma Ratnam

ABSTRACT

Artificial Intelligence (AI) is rapidly transforming the landscape of pulmonary diagnostics, offering innovative solutions to enhance disease detection, classification, and prognosis. With the increasing prevalence of respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, lung cancer, and interstitial lung diseases (ILDs), the need for accurate, timely, and efficient diagnostic tools has never been greater. AI technologies, particularly machine learning (ML) and deep learning (DL) algorithms, have demonstrated remarkable potential in analyzing complex medical data, including imaging studies, pulmonary function tests, and clinical biomarkers. In pulmonary imaging, AI enhances the interpretation of chest X-rays, computed tomography (CT), and high-resolution CT (HRCT) scans by improving the detection of lung nodules, interstitial changes, and signs of pulmonary infections with accuracy comparable to, or even surpassing, that of expert radiologists. Additionally, AI-driven models assist in risk stratification, lung cancer screening, and thequantification of disease progression. In pulmonary function testing, AI algorithms enable precise analysis of spirometry and other lung function parameters, supporting the early detection of obstructive and restrictive patterns that might be missed in traditional assessments. The integration of AI into clinical practice faces challenges related to data privacy, algorithm transparency, regulatory approval, and the need for large, diverse datasets to improve model generalizability. This review provides a comprehensive overview of the current applications of AI in pulmonary diagnostics and explores future directions, including the potential of AI in precision medicine, remote monitoring, and real-time diagnostic support. As AI continues to evolve, its role in pulmonary medicine will expand, promising improved patient outcomes through early detection, accurate diagnosis, and personalized care.

Keywords: Machine learning, Deep learning, Pulmonary diagnostics, Artificial intelligence.