AI in Telehealth Resources

Artificial Intelligence (AI) is revolutionizing telehealth by addressing persistent challenges in diagnosis, patient monitoring, and healthcare accessibility. This data evaluates AI's integration into telehealth systems, emphasizing its transformative role in enhancing diagnostic precision, personalizing treatments, and bridging gaps in healthcare equity. The study explores methodologies such as machine learning, natural language processing, and predictive analytics, presenting their impact on optimizing care delivery. Ethical implications and regulatory challenges are analyzed alongside future directions to establish AI as a cornerstone of digital healthcare innovation. Telehealth, the remote delivery of healthcare services via telecommunications technologies, has evolved into an important solution for addressing healthcare challenges. Factors such as the global burden of chronic diseases, aging populations, and disparities in healthcare access show the urgency for scalable and efficient healthcare delivery systems. Yet, traditional telehealth approaches often struggle with limitations in diagnostic accuracy, patient engagement, and resource allocation. The integration of Artificial Intelligence (AI) into telehealth systems presents a transformative opportunity to overcome these barriers. AI technologies—including machine learning (ML), natural language processing (NLP), and computer vision—enhance data analysis, automate repetitive processes, and enable more accurate and personalized healthcare delivery. For instance, ML models improve diagnostic precision by detecting patterns in medical imaging, while NLP facilitates seamless patient-provider communication through intelligent chatbots and multilingual support tools.