Digital Health Technologies in Medicine: Evidence, Artificial Intelligence Integration, and Ethical Challenges

Abstract

Digital health technologies (DHTs), including digital therapeutics (DTx), are revolutionizing patient care by enabling the prevention, management, and treatment of medical conditions. These tools comprise care delivery mobile applications, wearable devices, and cloud platforms for capturing real-time data and enabling remote monitoring. DTx also encompasses artificial intelligence (AI) and machine learning algorithms, as well as AI agents and digital twins (DTs) for clinical decision-making and predictive analytics. Evidence supports the effectiveness of DHT strategies across different clinical fields. For example, wearable and remote patient monitoring technologies enable continuous assessment and personalized feedback in cardiology and neurology. Additionally, AI-enabled devices are mostly implemented for continuous monitoring of glucose levels. However, several key challenges remain. Persistent gender and social biases in datasets and algorithms raise ethical concerns, particularly for underrepresented groups and pediatric populations. Mitigation strategies include regulatory frameworks, explainable AI, and trustworthy AI ecosystems. This narrative review synthesizes current evidence, highlights implementation barriers, and proposes recommendations to enhance inclusivity, interoperability, and real-world evaluation of these technologies. Applications of DHT in animals within the one digital health framework are also discussed.

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