Policy
Artificial intelligence will reshape medicine and public administration within a decade. The question is whether Britain leads that transition or follows it.
Marc is one of a small number of politicians who has actually built AI tools for clinical use. At an AI diagnostics company, he led the development of machine learning systems for cancer detection that achieved FDA clearance and CE marking, meaning they met the regulatory bar required for use on real patients in the United States and Europe. At the Cabinet Office, he assessed how artificial intelligence was being implemented across government departments including the MoD and Public Health England. He brings to the policy debate something that most contributors to it lack: direct, hands-on experience of what AI can and cannot do in high-stakes environments.
His view is that the governance conversation around AI in healthcare has become detached from the practical reality of how these tools are developed and deployed. Regulators, commissioners, and politicians are making decisions about AI without understanding the difference between a validated clinical algorithm and a consumer chatbot. That gap is dangerous.
Marc wants to see a regulatory framework for AI in healthcare that is rigorous without being obstructive, that learns from the FDA's approach while improving on it, and that positions the NHS as a destination for AI innovation rather than a graveyard for pilots.
On government AI more broadly, he believes the Cabinet Office's approach has been too cautious and too siloed, and that the productivity gains available from well-governed AI adoption across public services are being left on the table.