Digital mental health technologies and the changing face of regulation
According to recent data from the World Health Organization (WHO), over 1 billion people worldwide are living with mental health conditions right now, with depression and anxiety alone costing the global economy an estimated US$ 1 trillion each year.[1] Specifically in the UK, an ongoing survey found that 1 in 6 participants reported having been diagnosed with depression at some point, and 1 in 7 with anxiety, with 10% reporting moderate to severe depressive symptoms in the previous fortnight.[2]
These numbers emphasise the widespread and growing burden of mental health, causing traditional mental health services, already under strain, to struggle to meet demand. As a result, interest in more scalable, accessible alternatives is spiking and Digital Mental Health Technologies (DMHTs), ranging from apps and online platforms to AI-powered tools, are fast being seen as essential to help closing the widening treatment gap. In fact, the global market for emerging mental health devices and platforms was valued at $3.22 billion in 2023 and is projected to reach $17.70 billion by 2031.4
The rise of DMHTs
DMHTs come in many forms, including smartphone apps, web-based platforms, wearable tools, AI chatbots, and even virtual reality therapy or headsets.
These tools offer the promise of a more accessible, flexible, and personalised mental health support, directly available to users or with a referral from clinicians or educators. As the demand for mental health support continues to outpace the capacity of traditional health systems, DMHTs are increasingly being adopted both by individuals and health services.
DMHTs sit within a broader wave of digital health technologies, helping manage a range of issues such as asthma with smart inhalers, diabetes with Continuous Glucose Monitors (CGMs), blood pressure via hypertension monitoring platforms, heart conditions via wearable cardiac monitors, and post-surgery recovery via trackers and remote physiotherapy.
However, this rapid expansion raises critical questions: When does a digital tool become a medical device? When this happens, what safety, efficiency and regulatory standards should it meet?
What counts as a medical device?
In February 2025, the Medicine and Healthcare products Regulatory Agency (MHRA) published new guidance that explicitly targets DMHTs and their classification as a medical device: “Digital mental health technology: qualification and classification.”[3] Produced as part of the Wellcome Trust project part of a £1.8 million investment by the UK government, the document contributes to wider efforts to develop an appropriate regulatory framework for digital mental health tools.
This guidance marks a major step forward, offering for the very first time, clear criteria for when these digital tools must be treated as medical devices under UK law. The direction depends primarily on two factors:
- Intended purpose, i.e., what the developer claims the tools are to be used for (e.g., general wellbeing or diagnosing/treating a mental health condition)
- Functionality, i.e., how the tool works. For instance, whether it simply provides education or actively analyses data, provides AI-driven assessment, or influences treatment decisions
This means that if a DMHT is intended for a medical purpose and includes complex functionality, it must be regulated as a Software as a Medical Device (SaMD) and the manufacturer will have to meet stringent standards for safety, performance, and clinical evidence. However, a DMHT with a medical purpose, but low functional impact could well be excluded from medical device regulation compliance as it does not provide a clinical effect or influence patient care decisions.
In fact, under this framework, low-risk tools such as simple wellbeing apps or basic screening questionnaires, may self-certify as a Class I medical devices, while higher-risk tools like AI chatbots that can contribute to the diagnosis or treatment of mental health conditions will be classified as Class IIa, Class IIb, or even Class III and require review by a Notified Body before entering the UK market.
The guidance also lays out how classification should consider labelling, instructions for use, promotional materials, and any other information that shapes intended use and functionality. This clarity is especially important now that DMHTs are becoming increasingly more sophisticated, with many integrating AI, machine-learning algorithms, adaptive user assessment, and even clinical recommendations.
What does this mean for products already on the market?
An important and often overlooked implication of the MHRA’s guidance is its relevance not only to new DMHTs, but also to products already on the market. Many DMHTs have evolved rapidly since launch, expanding functionality, incorporating AI driven features, or shifting their claims from general wellbeing to clinical support. In doing so, some products may now fall outside their original regulatory assumptions.
For example, a mental health app initially positioned as a wellbeing or resilience tool may have self certified as a Class I medical device or avoided medical device classification altogether. If that same product later introduces AI based mood analysis, risk stratification, or outputs used by clinicians to inform diagnosis or treatment decisions, it may now meet the criteria for a higher risk Software as a Medical Device classification, such as Class IIa or IIb.
In practical terms, this may trigger a need for regulatory reassessment, reclassification, additional clinical evidence generation, and enhanced quality management systems. Developers should therefore be cautious about “functional drift”, where incremental feature additions unintentionally move a product into a higher risk regulatory category. The MHRA guidance reinforces that regulatory status is not static and must be reviewed as products evolve.
The bigger regulatory picture
The new DMHT guidance is part of a broader regulatory reform under the Software and AI as a Medical Device Change Programme,[4] launched by the MHRA in October 2022. The Change Programme aims to modernise and streamline regulation of all software-based medical devices, recognising the specific challenges posed by AI-driven medical devices. These include algorithm updates management, the risks associated with continuous or adaptive learning, the need for rigorous post-market surveillance and the growing expectation around transparency and explainability in clinical contexts.
In the US, the FDA has issued its own guidance, including the Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan,[5] outlining the agency’s intention to develop a more predictable regulatory framework of AI-enabled medical software. However, unlike the MHRA’s framework, the FDA anticipates evaluating such technologies on a case-by-case basis, taking into account the device’s risk classification, intended use, and degree of autonomy.
In the EU, the introduction of the EU AI Act, effective 1st August 2024, has established a comprehensive regulatory framework for AI across all sectors, including healthcare. Under the Act, AI systems used as components of medical devices are generally categorised as high-risk, triggering stringent obligations such as implementation of formal risk-management systems, rigorous data governance, and extensive technical documentation.
This shows a widespread commitment to formally integrate DMHTs into the medical-device ecosystem, with dedicated regulation intended to safeguard patients while still enabling innovation.
Commercial reality: Reimbursement and sustainability
From a commercial perspective, reimbursement remains one of the most challenging aspects of the digital mental health landscape. Unlike traditional medical devices or pharmaceuticals, many DMHTs lack a clear national reimbursement route. Adoption is often driven through pilots, local commissioning decisions, or innovation budgets, rather than standardised funding mechanisms.
Where reimbursement has been achieved, it is typically linked to a clear demonstration of health system value, such as reduced clinician workload, improved patient outcomes, avoidance of hospital admissions, or measurable cost savings. As a result, the most commercially successful DMHTs are those that position themselves not simply as digital tools, but as solutions that address system level pressures.
This places increased emphasis on robust clinical and economic evidence, alongside early engagement with healthcare providers and commissioners to understand how value will be assessed and funded in practice.
For innovators, the message is clear: purpose must be well defined, safety designed in from the outset, evidence generated, and regulatory scrutiny anticipated, rather than resisted. For users and patients, the promise is improved and safer access to mental-health support that extends beyond the constraints of traditional services. At a public-health level, digital tools have the potential to help close the treatment gap, expand access, and relieve pressure on already overburdened systems.
Realising this potential, however, will depend on responsible development, transparent regulation and rigorous evaluation. As digital mental health technologies evolve and AI becomes increasingly central, sustained oversight and adaptive regulatory frameworks will be critical to ensure innovation delivers meaningful and lasting benefit.
Understanding the adoption landscape: How do innovators know what is already being evaluated?
For developers seeking to understand where their product sits within the current adoption landscape, several UK mechanisms provide useful signals. Organisations such as NICE play a central role in evaluating digital health technologies through evidence standards frameworks, Medtech Innovation Briefings, and formal evaluation programmes. These pathways help identify technologies that are considered sufficiently mature for NHS use and highlight the types of clinical and economic evidence expected.
Monitoring these initiatives, alongside NHS innovation pilots and Integrated Care System programmes, can offer valuable insight into whether a digital mental health technology is aligned with current health system priorities or remains at an early experimental stage. Absence from these pathways may indicate that further evidence generation or refinement is needed before large scale adoption is realistic.
From regulation to real world impact
As DMHTs move from experimentation to mainstream care, success will increasingly depend on more than innovation alone. Regulatory clarity is beginning to emerge, but developers must also demonstrate clinical credibility, system readiness, and sustainable value for healthcare providers. In this environment, those who treat regulation as a strategic enabler, rather than a compliance hurdle, will be best positioned to scale responsibly, secure adoption, and deliver meaningful impact for patients, health systems, and society.
References
[1] WHO, “Over a billion people living with mental health conditions – services require urgent scale-up”, 2nd September 2025, https://www.who.int/news/item/02-09-2025-over-a-billion-people-living-with-mental-health-conditions-services-require-urgent-scale-up
[2] Our Future Health, “Revealed: what our data says about the UK’s mental health”, 20th June 2025, https://ourfuturehealth.org.uk/news/2025-mental-health-statistics/
[3] GOV.UK, “Digital mental health technology: qualification and classification”, 3rd February 2025, https://www.gov.uk/government/publications/digital-mental-health-technology-qualification-and-classification
[4] GOV.UK, “Software and AI as a Medical Device Change Programme roadmap”, 14th June 2023, https://www.gov.uk/government/publications/software-and-ai-as-a-medical-device-change-programme/software-and-ai-as-a-medical-device-change-programme-roadmap
[5] FDA, “Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan”, January 2021, https://www.fda.gov/media/145022/download?attachment
About the author
Parminder Kalle is chief commercial officer at IMed Group, with more than two decades of experience across medical devices, in vitro diagnostics, pharmaceuticals, and digital health. He provides strategic and commercial leadership on regulatory, quality, and clinical frameworks, helping organisations shape viable pathways for AI-enabled digital health and Software as a Medical Device (SaMD). Kalle works with senior leadership teams to align regulatory strategy, evidence planning, and market adoption, supported by specialist delivery teams.
