Across the globe, more than one billion people are currently living with a mental health condition. The economic cost of depression and anxiety alone is estimated at around one trillion U.S. dollars every year. Prevalence rates are high across many regions, with a substantial proportion of adults experiencing depression, anxiety, or clinically significant symptoms at some point in their lives.

In many health systems, demand for mental health care continues to rise faster than treatment capacity, creating long waiting lists and limited access to care. In this context, Digital mental health technologies (DMHTs) have moved rapidly from the margins to the center of policy discussions, investment strategies, and clinical innovation.

Digital Health as a Response to System Pressure

DMHTs encompass a broad and expanding range of tools, including mobile apps, online platforms, wearable tools, AI chatbots, and immersive technologies such as virtual reality. Unlike traditional services, digital tools can be accessed remotely, used at any time, and adapted to individual users. For overstretched health systems, they promise earlier intervention, reduced clinician burden, and improved continuity of care.

Digital mental health technologies are part of a wider transformation in healthcare delivery, where software and connected devices are increasingly used to manage various conditions. Smart inhalers support asthma management, continuous glucose monitors assist people with diabetes, wearable cardiac monitors track heart rhythms, and remote physiotherapy tools guide rehabilitation after surgery.

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When Does a Digital Tool Become a Medical Device?

As digital tools become more sophisticated, it becomes more difficult to determine at what point a mental health app or platform goes from being a wellbeing product to a regulated medical device. This is critical to ensuring that the product is able to remain on the market and does not have to go through costly recalls, for example.

In early 2025, the MHRA published new guidance specifically addressing this issue. Developed as part of the Wellcome Trust project supported by government funding, the guidance provides clarity on how digital mental health technologies should be assessed, qualified, and classified under UK medical device regulations. The guidance is based on two core factors: intended purpose (what the developer claims the product is designed to do) and functionality (how the technology operates in practice).

Based on this document, if a digital mental health tool has a medical purpose and performs functions that can influence diagnosis or treatment, it is likely to be classified as Software as a Medical Device (SaMD) and regulated as such.

Risk-Based Classification and Regulatory Expectations

The MHRA’s approach reflects a risk-based model, meaning low-risk products, such as basic screening questionnaires or simple wellbeing applications, may fall outside medical device regulation altogether or self-certify as Class I devices, while higher-risk tools, including AI-enabled chatbots that contribute to diagnosis or treatment planning, may be classified as Class IIa, IIb, or even Class III and therefore require an assessment by a Notified Body before they can be placed on the UK market.

This guidance also makes clear that classification is influenced not only by technical features, but also by how products are presented to users; labelling, instruction for use, promotional materials and user interface all contribute to defining intended purpose and functional impact. This is particularly relevant as DMHTs increasingly incorporate machine learning, adaptive algorithms and personalised recommendations.

Implications for Existing Products

The MHRA guidance is retroactive and therefore relevant to products already on the market. Many digital mental health tools have evolved rapidly since launch, adding new features or shifting focus to meet market demand or in response to technological advances, and, for example, a general wellbeing app may now include mood analysis, risk stratification, or outputs designed for clinician review.

Such changes can alter a product’s regulatory status, and what was once a low-risk tool may now meet the criteria for a higher-risk classification, triggering the need for reassessment, additional clinical validation, and more robust quality management systems. To avoid exposure to compliance risks due to unintended “functional drift”, developers must actively monitor how changes in functionality, claims, or the user base affect classification.

International Regulatory Landscape

The UK’s approach sits within a broader international effort to modernize regulation to include software-based and AI-enabled medical devices, addressing challenges such as algorithm updates, adaptive learning systems, post-market surveillance, and transparency in AI-driven decision making.

In the United States, the Food and Drug Administration (FDA) has outlined its own strategy for AI- and ML-based medical software, emphasizing a flexible, case-by-case assessment based on risk and intended use. Meanwhile, the European Union’s AI Act establishes a comprehensive framework for AI regulations. Under the Act, AI systems used in medical devices are generally considered high risk, triggering stringent requirements for risk management, data governance, and technical documentation.

From Regulation to Real-World Impact

As digital health technologies mature, success will depend on more than just technical innovation. Clear regulatory frameworks are beginning to emerge, but developers mist also demonstrate clinical credibility, measurable economic value, and operational readiness. DMHTs manufacturers who engage proactively with regulation, viewing it as an enabler rather than a barrier, are more likely to scale responsibly and earn the trust of users, clinicians, and regulators.

Monitoring NHS pilot programmes, Integrated Care Systems initiatives, NICE frameworks and assessments programmes, and regional innovation schemes can also provide valuable insight into current priorities.

Realizing the potential of DMHTs will ultimately require sustained oversight, transparent governance, and rigorous evaluation to ensure innovation delivers meaningful, lasting benefits for everyone.