Transforming telehealth with Artificial Intelligence: Predictive and diagnostic advances in remote patient care
Year: 2025
Type: article
Abstract: The rapid evolution of telehealth, accelerated by the global demand for remote medical services, has opened new avenues for integrating Artificial Intelligence (AI) into healthcare delivery. This paper examines how AI is fundamentally reshaping telehealth and remote patient monitoring (RPM) through advanced diagnostic tools and predictive modeling. By leveraging technologies such as machine learning, natural language processing, and deep learning algorithms, healthcare providers can now extract actionable insights from complex medical data, including electronic health records (EHRs), patient-generated data from wearable devices, and real-time physiological signals. AI-driven systems can detect early signs of chronic disease progression, forecast patient deterioration, and generate personalized treatment plans, thereby enhancing clinical decision-making and reducing the burden on overextended healthcare systems. Additionally, AI chat bots, voice recognition systems, and virtual assistants are improving patient-provider communication and automating routine tasks, leading to improved access and operational efficiency. The paper also discusses real-world applications of AI in virtual triage, automated diagnostic imaging, and remote behavioral health assessments. It further addresses the ethical and technical challenges of deploying AI in telehealth, such as ensuring data security, mitigating algorithmic bias, maintaining patient trust, and achieving seamless integration with legacy healthcare infrastructure. Overall, this study underscores the transformative potential of AI in virtual healthcare, offering a pathway toward more proactive, equitable, and patient-centered care delivery in both urban and underserved regions.
Authors: Shaharia Ferdausi, Kanis Fatema, Md Rakib Mahmud, Md Refadul Hoque, Md Musa Ali
Institution: St. Francis College
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4412472602