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 are regulated, software-based interventions that deliver evidence-supported therapeutic effects; artificial intelligence (AI) and machine learning, including advanced architectures, such as agentic systems and digital twins, may augment DTx workflows but are not defining features of DTx. Growing 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 widely 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 work is a narrative, expert-driven review based on illustrative literature curated by domain specialists. It aims to synthesize current evidence, highlight implementation barriers, and propose recommendations to enhance inclusivity, interoperability, and real-world evaluation of digital health technologies. Applications of DHTs in animals within a One Digital Health framework, as well as potential applications in infection-related oncology, are also discussed. Not applicable.
Cascella et al. (2026) studied this question.