Cognitive Computing in Respiratory Health: Revolutionizing Medicine Through Artificial Intelligence(AI) And Machine Learning (ML)

Abstract

Human errors in medical practice can lead to misdiagnosis, resulting in inappropriate treatment and serious risks to patient health. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as a valuable tool for reducing such errors, particularly in medical diagnostics. These technologies can rapidly and accurately analyze large volumes of data, providing additional insights that help healthcare professionals to make more accurate decisions. AI excels in providing solid evidence to guide clinical decisions, reducing reliance on subjective judgments. It can analyse complex datasets and identify patterns that has chances to be overlooked by human eyes, thus leading to improved diagnostic accuracy and treatment plans. ML, a component of AI, uses adaptive models that learn from extensive datasets, though these models must be trained on high-quality data to avoid perpetuating errors or biases. In pulmonary medicine, AI and ML have shown considerable potential in diagnosing and treating conditions such as asthma, chronic obstructive pulmonary disease (COPD), and pulmonary fibrosis. These technologies help determine disease staging, forecast exacerbations, and estimate survival rates. By harnessing AI and ML, clinicians can make more precise diagnoses, customize treatments to individual needs, and detect early signs of worsening conditions, ultimately enhancing patient outcomes. Moreover, AI and ML can minimize patient risks by providing a broader and more in-depth analysis of medical data. This review explores how these technologies can process large datasets to deliver insights that surpass human capability, fostering error-free diagnosis and treatment.