Georgia Ling recently attended the Economist Impact’s AI in Health Summit 2025, where policymakers, healthcare and MedTech providers, and investors convened to discuss how to unlock the potential of AI in healthcare. The real question to debate here is, what is going to tip the balance to drive AI adoption at scale in the health sector?
1. No time to waste on data build
“Good AI” depends on “good data.” Yet what constitutes “good” varies depending on the problem at hand. We all know that progress in healthcare is slower due to legacy systems, siloed and inconsistent data entry across trusts, the multi-modal nature of clinical information, and the sensitivities of patient privacy. This serves to highlight the scale of the opportunity and the appetite for solutions, but also the reality of the challenges at hand. The £1.9bn Frontline Digitalisation Programme and the £10bn NHS Digital Transformation budget for 2025–2029 are steps in the right direction, whereas cross-industry partnerships can help to accelerate standard-setting and data interoperability. HealthTech firms that can help solve the data problem first will be the most valuable.
Once these foundations are in place, it will open up new data streams (and revenue streams) through the rise of wearables and privacy-enhancing technologies – creating trust and enriching doctor-patient relationships, as well as health outcomes.
2. Expectations befall reality for AI adoption
Has the promise of AI been realised? At the Summit one hospital reported access to 120 AI tools yet active use of only 5, largely because most were poorly integrated into workflows or misaligned with clinicians’ needs. Adoption of AI tools hinges on expectations and taking people on the journey. What is the promise that is being made? Is it cutting down bureaucracy, improving insight, or more time for patient care? Involving clinicians early in product design helps surface the real pain points and accelerates adoption.
The same logic applies across the healthcare ecosystem. Payers, providers, and pharma companies are drawn to AI tools that streamline decision-making, reduce uncertainty, and improve pricing or commercial efficiency. When a solution directly accelerates speed to market, sharpens negotiation outcomes, or increases ROI, the incentive to adopt becomes self-evident. Every unused tool is a sunk cost – and a missed opportunity for better care and outcomes.
3. Balancing innovation with trust
Regulators know that they are playing catch-up to innovators in AI. They recognise the need to facilitate innovation and to encourage experimentation, but equally the need for guardrails in place as patient and clinician trust remains the bedrock for adoption. Thoughtful debate about what constitutes an “acceptable” risk profile in different settings is therefore central to the future of AI adoption. However, given the pace of change and development, will regulators realistically keep up with the cutting edge?
Conclusion
The Summit highlighted the enormous opportunity ahead if the promise of AI is unlocked. Better data foundations, a deep understanding of the pain points in healthcare and regulatory clarity will be critical enablers, but trust, collaboration, and a willingness to rethink entrenched processes are foundational to innovation, while ensuring patient safety and adoption.
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