Telemedicine and artificial intelligence (AI) integration has revolutionized the healthcare system through accurate diagnosis, effective treatment, and remote consultations. Some of the technologies used in AI include machine learning algorithms and natural language processing technology, which help algorithms offer predictive analytics and personalized care. In addition, these technologies have reduced the clinical staff’s work burden and have led to increased patient engagement. However, despite these skyrocketing forward movements, AI-driven telemedicine faces challenges such as data privacy threats, bias in algorithm use, and the absence of harmonization between different platforms. Implementing these limitations is among the most significant factors that make telehealth services ethical, fair, and scalable. It is therefore essential to analyze the new role of AI in telemedicine, list the advantages and possible risks, and provide strategic recommendations for addressing current challenges. The findings hope to enlighten healthcare executives, legislators, and researchers on the opportunities and challenges of AI in the telemedicine sector.
Keywords: AI, Data Privacy, Digital healthcare, Patient outcomes, predictive analytics, telemedicine
