TechDental Intelligence Briefing | 26 May 2026 | 12 min read
Listen to the Intelligence Briefing: 🎧 [Spotify] 🎧 [Apple Podcasts] ▶️ [YouTube]
Direct answer: UKRI has committed a record £1.6 billion to the AI sector between 2026 and 2030. The NHS 10-Year Health Plan, published in July 2025, sets out an explicit ambition to become the world's most AI-enabled health system. These are not incremental commitments. They are structural decisions about which verticals get embedded into the next generation of UK health infrastructure. Dentistry, a sector that delivered 35 million courses of NHS treatment in 2024/25 alone, is almost entirely absent from both conversations. That is not a coincidence. It is a consequence of a gap in representation that this industry has not yet moved to close. And the cost of that absence is not theoretical. It is measurable, it is compounding, and it will arrive on the balance sheets of every practice, group, and founder building in this space within the decade.
The Scale of What Is Being Built
The numbers matter because they establish the context for everything else.
In February 2026, UKRI published its first-ever AI strategy, backed by £1.6 billion of funding targeted directly at the AI sector between 2026 and 2030. This is UKRI's biggest single investment area over the spending period. It covers six priority areas: advancing technology development, transforming research through AI, developing AI skills and talent, accelerating innovation for economic growth, championing responsible AI, and building world-class data and infrastructure.
The NHS 10-Year Health Plan, published by the Department of Health and Social Care in July 2025, goes further. It describes five transformative technologies as strategic priorities for the NHS: data, artificial intelligence, genomics, wearables, and robotics. Its stated goal is to make the NHS the most AI-enabled health system in the world. The Spending Review 2025 separately committed £10 billion for NHS technology and digital transformation by 2028/29.
These are not aspirational documents. They are funding commitments backed by Spending Review settlements. The decisions being made right now about which clinical pathways get AI co-pilots, which conditions get included in the Single Patient Record architecture, and which verticals receive data infrastructure investment will shape what is possible in UK healthcare for the next fifteen years.
Dentistry is not visible in any of it.
The Data Dentistry Is Sitting On
It is worth being precise about what is actually at stake, because the dental sector consistently underestimates its own strategic position.
According to the NHSBSA Dental Statistics for England 2024/25, published in August 2025, 35 million courses of NHS dental treatment were delivered in 2024/25, a 4% increase on the previous year. 18 million adults received NHS dental care in the 24 months to 31 March 2025. 6.9 million child patients were seen in the 12 months to 31 March 2025. The NHS dental workforce stood at 24,543 dentists with NHS activity.
Those are not small numbers. They represent a clinical data asset of significant scale, generated at every interaction: examination findings, radiographic assessments, treatment decisions, recall intervals, patient demographics, and disease prevalence data. The National Dental Epidemiology Programme's 2024 survey found that 26.9% of five-year-olds in England had signs of dental decay. That is a population health data point with direct implications for preventive care models, health inequality analysis, and AI-assisted early intervention pathways.
The NHS 10-Year Health Plan explicitly identifies preventive care and health inequality as central to its transformation agenda. Dental disease is among the most preventable conditions in the entire health system. The data required to build effective AI tools for dental prevention, risk stratification, and treatment optimisation already exists within NHS dental records, in volume, at a scale that would support serious machine learning work.
It is not being used for that purpose. Not because the opportunity is absent. Because no credible infrastructure has been built to surface it.
What Science Diplomacy Actually Means
Most people hear the phrase science diplomacy and picture summits. Heads of state shaking hands in front of flags. Joint declarations about shared values. That is not what science diplomacy is. That is politics with a scientific backdrop.
Real science diplomacy is quieter and far more consequential. It is the sustained, institutional work of building research relationships across borders so that when funding frameworks are designed, when data standards are set, when bilateral agreements are signed, your discipline, your technology, and your clinical evidence are already inside the room. It is infrastructure work. It is slow, unglamorous, and it compounds.
The concept has formal roots in the Royal Society's 2010 framework, which defined science diplomacy across three dimensions. Science in diplomacy, where scientific advice informs foreign policy. Diplomacy for science, where diplomatic activity facilitates international research collaboration. And science for diplomacy, where scientific cooperation builds broader relationships between nations. All three dimensions are active in the UK-Türkiye relationship right now. None of them currently include a dental dimension.
The British Council's International Research and Empowerment Programme (IREP), operating under the International Science Partnerships Fund (ISPF), is a live example of how this infrastructure operates in practice. It funds bilateral research and innovation collaborations between UK institutions and partner countries, with artificial intelligence confirmed as a primary thematic priority. Türkiye is a confirmed partner country.
For the past six months I have been a mentor on the Rising Women Leaders in AI programme (RWLAI), a bespoke leadership development and research initiative fully funded by the British Council's IREP under the ISPF (Grant reference: ISPF/IREP ref. 2146 - Kudret Türkiye). The programme is led by Dr. Selin Kudret, Principal Investigator and UK Programme Lead at the University of Reading's Henley Business School, and Professor Cigdem Gunduz Demir, Türkiye Programme Lead at Koç University, in partnership with Thames Valley AI Hub. Its purpose is to tackle the structural and everyday barriers that deter talented women in AI from emerging and staying as effective leaders, equipping them with the capacity, confidence, and networks to thrive across the UK-Türkiye research corridor.
The programme delivers evidence-based executive education across leadership in turbulent times, responsible AI, navigating gender bias, and leading multicultural teams, combined with a structured UK-Türkiye mentoring bridge designed to transfer knowledge, networks, and sponsorship across R&D, product, data, and risk. It directly addresses three UN Sustainable Development Goals: SDG 5 Gender Equality, SDG 8 Decent Work and Economic Growth, and SDG 10 Reduced Inequalities.
Sitting inside that programme has given me a ground-level view of how science diplomacy actually operates. The relationships, the research frameworks, the institutional trust built across borders through programmes like RWLAI, these are the infrastructure that determine which clinical verticals get embedded into the next generation of health AI and which ones get added later, at significantly greater cost, under constraints they had no hand in designing.
Dentistry is not present in any of these conversations. Not because the opportunity is absent. Because nobody has yet walked it into the room.
Why Türkiye Matters for This Conversation
Türkiye is not an incidental partner. It has a young, large population, a rapidly expanding private healthcare sector, and a government that updated its National AI Strategy in 2024 with an explicit commitment to scaling AI pilot projects in healthcare as one of nineteen concrete actions in its 2024/25 action plan. The UK-Türkiye bilateral relationship in research and innovation is active and growing. The RWLAI programme is itself a demonstration of what that relationship can produce when both sides commit institutional resource and leadership credibility to a shared goal.
The dental AI dimension of that relationship is, at present, essentially zero. And that is a more significant gap than it might initially appear.
Türkiye's private dental sector has expanded rapidly over the past decade, driven by rising middle-class demand, medical tourism at significant scale, and government investment in healthcare infrastructure. Turkish dental schools produce a large clinical workforce annually. The country has population-level oral health data across a demographic profile that is meaningfully different from the UK's, which creates precisely the kind of training data diversity that dental AI diagnostic tools require to perform robustly across different patient populations.
The clinical parallels are also striking. Both the UK and Türkiye face significant preventable dental disease burdens concentrated in child and lower-income adult populations. Both governments have identified preventive healthcare as a strategic priority. Both are investing in digital health infrastructure. A bilateral research collaboration focused on AI-assisted early detection of dental caries in child populations would address a shared clinical problem, generate comparative data across two distinct demographic cohorts, and produce peer-reviewed evidence with direct implications for NICE evaluation pathways and MHRA clinical AI guidance in the UK.
That collaboration does not yet exist. It could be initiated through the ISPF framework for less than the cost of a mid-tier sponsorship at a UK dental trade show. The strategic value would be categorically different.
What Dental AI Could Look Like Across Borders
This is where aspiration becomes concrete, because the specific opportunities are available now and the window for each is time-limited.
The RWLAI programme has demonstrated that a structured UK-Türkiye knowledge bridge, designed with rigour and governed with care, produces real outcomes for the people inside it. The same model applied to dental AI research would create a bilateral infrastructure that neither country has at present. Not a memorandum of understanding. Not a joint press release. A functioning research collaboration with shared data, shared governance, and shared clinical ambition.
A joint radiographic AI training dataset, developed collaboratively between UK and Türkiye clinical institutions, would create a resource that neither country could build as effectively in isolation. Dental AI diagnostic tools trained on more diverse datasets perform better, pass regulatory scrutiny more credibly, and command stronger commercial positions in international markets. The diversity of the training data is not just a clinical asset. It is a commercial one.
A bilateral governance framework for dental AI ethics and clinical regulation, developed through a science partnership programme and anchored to both UK and EU regulatory contexts, would give founders building in this space a compliance architecture that works across multiple markets from day one. That is a material reduction in the cost and complexity of international expansion for any UK dental AI company with ambitions beyond the domestic NHS market.
A joint preventive care AI pilot, testing AI-assisted risk stratification for caries and periodontal disease across matched UK and Türkiye child population cohorts, would generate the kind of multi-jurisdictional clinical evidence that NICE, MHRA, and international regulatory bodies increasingly require before recommending AI tools for NHS-scale deployment. Building that evidence base through a bilateral partnership costs a fraction of what it would cost to generate it in a single-country trial at sufficient scale.
None of this requires a new organisation. It requires a small number of credible individuals deciding to show up in the institutional spaces where this infrastructure is being designed.
What the Absence of Dental From AI Policy Actually Costs
The cost of absence from infrastructure design is not immediately visible. That is precisely what makes it dangerous.
When the NHS builds its data architecture for the Single Patient Record, the fields it includes and the standards it adopts will reflect the clinical areas that were actively represented in the design process. When UKRI allocates its £1.6 billion across healthcare AI research, the specific conditions and clinical pathways that receive funding will reflect the priorities that were put in front of the programme offices. When bilateral science partnerships define their thematic priorities, the verticals that get included will be the ones whose communities showed up.
Dentistry showing up later, after the architecture has been set, does not produce the same outcome as dentistry being present during the design phase. Late inclusion means integration under constraints built for acute medicine rather than primary care. It means retrofitting data standards, adapting governance frameworks, and building on infrastructure that was optimised for hospitals rather than dental practices. The cost of that retrofit, in time, money, and lost clinical opportunity, falls on practices, patients, and ultimately the NHS budget.
This is a systems problem, not a technology problem. The technology for dental AI exists and is advancing rapidly. The constraint is not computational. It is institutional. Dental AI cannot reach its potential within the UK health system if the people building that system do not understand what dental AI can do, or if they have simply never been asked to include it.
The Practical Opportunity
There are specific, concrete entry points available to the dental sector right now that do not require a lobbying campaign or a parliamentary working group.
The DSIT AI ecosystem and the UKRI programme offices are accessible to credible voices from clinical sectors. Thames Valley AI Hub and comparable regional science and innovation networks provide routes into the policy conversation at a tier below Westminster, where much of the practical infrastructure work actually happens. The British Council IREP bilateral programme is open to UK research institutions seeking Türkiye partners, with AI confirmed as a thematic priority for the current funding cycle.
A research collaboration grant between a UK dental school and a Türkiye university, focused on AI-assisted preventive care or diagnostic accuracy, is a realistic and fundable proposition right now. The application process is established. The thematic fit is confirmed. The clinical case is strong. What is missing is a dental institution willing to make it.
None of this requires the dental sector to transform itself overnight. It requires a small number of credible individuals to be present in the right rooms, making the case that dental data is a serious health asset, that dental AI is a serious clinical opportunity, and that the UK cannot credibly claim to be building the world's most AI-enabled health system while one of its largest primary care sectors remains entirely off the map.
What This Means for Founders and Investors
For founders building dental AI products in the UK, the policy context matters more than it might initially appear. The NHS data frameworks, the NICE evaluation pathways, the MHRA clinical AI guidance, and the interoperability standards being developed under the Single Patient Record programme will all shape what is commercially viable in this sector. A product built without reference to the regulatory infrastructure being designed right now will face a harder path to NHS adoption than one developed in alignment with it.
For investors evaluating dental AI opportunities, the UK market presents a specific structural dynamic. The founders who understand the policy landscape, who have built relationships with the DSIT AI ecosystem and NHS England's digital transformation teams, and who can demonstrate that their products were developed with UK clinical governance in mind, will be better positioned to convert clinical validation into NHS-scale deployment.
The window for influencing the infrastructure design is open now. It will not remain open indefinitely. The UKRI commitment runs to 2030. The NHS 10-Year Health Plan is being operationalised this year. The bilateral science partnership calls are funded on an annual cycle.
Dental AI does not have a technology problem. It has a representation problem. And representation problems are solvable, by the people willing to walk into the room.
About TechDental
TechDental is a strategic intelligence platform for founders, executives, operators and investors shaping the future of dentistry. Through high-level analysis and systems-focused conversations, we explore how AI, governance frameworks and operating model design influence performance, scalability and enterprise value in dental organisations.
LinkedIn: https://www.linkedin.com/in/drrandeep/
The future belongs to those who deploy technology with discipline.
Sources: UKRI AI Strategy, February 2026 (UKRI/DSIT). NHS Fit For the Future: 10 Year Health Plan for England, July 2025 (DHSC). NHS Dental Statistics for England 2024/25 (NHSBSA, August 2025). British Council IREP/ISPF Research Collaborations Programme, 2025 (British Council/DSIT). RWLAI Mentoring Charters, Version 02/12/2025 (Rising Women Leaders in AI, Grant ref. ISPF/IREP ref. 2146 - Kudret Türkiye). National Dental Epidemiology Programme Oral Health Survey of 5 Year Old Children 2024 (OHID). Türkiye National AI Strategy 2021/25 updated action plan (Turkish Presidency Digital Transformation Office). Royal Society New Frontiers in Science Diplomacy, 2010 (The Royal Society).
Copyright Dr Randeep Singh Gill and RIG Enterprises Limited (Company No. 11223423) 2026. All rights reserved. TechDental is a trading name of RIG Enterprises Limited.