Artificial Intelligence in HealthTech: direction, regulation & ethics

AI is already reshaping healthcare but without clear direction and regulation, its full potential could remain out of reach.

At the Compass Carter Osborne HealthTech round table, industry leaders discussed how to harness AI effectively while safeguarding safety, equity, and innovation. 

The need for a shared vision 

AI’s potential in healthcare is vast, from predicting health risks to optimising patient pathways. But panellists stressed the need for a coordinated, strategic direction. Without consistent goals or motivation, AI development risks becoming fragmented, with solutions that are difficult to scale or trust. 

Participants called for a national vision: one that defines how AI fits into healthcare systems, aligns stakeholders, and accelerates adoption without compromising care quality. 

Navigating regulation: UK vs EU 

Regulation is a double-edged sword. While it ensures safety, it can also slow progress. The EU is advancing quickly on AI safety and data protection, but some panellists noted that its framework may unintentionally hinder innovation, especially for start-ups. 

For the UK, there’s a narrow but valuable opportunity: to establish a “sandbox” environment where health innovators can safely trial AI without being overburdened by red tape. However, this requires political will and proactive policymaking, something still lacking. 

Predictive vs generative AI 

Not all AI is created equal. Predictive AI used for triaging, scheduling, or risk assessments is relatively well-understood and regulated. Generative AI, however, is far more complex. Foundation models evolve rapidly, making it difficult to evaluate performance or ensure reliability in clinical contexts. 

This adds urgency to calls for regulation that is adaptive and dynamic, capable of keeping pace with fast-changing technologies while preserving public trust. 

Getting the fundamentals right 

AI won’t succeed in isolation. It needs: 

• Robust infrastructure: quality data, interoperable systems, and trained users. 

• Clear accountability: who is responsible when AI makes a mistake? 

• Trust and transparency: patients and clinicians must understand how it works. 

The panel agreed: AI in healthcare holds enormous promise but without clear leadership, smart regulation, and ethical clarity, that promise could easily be squandered. 

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