
The House of Lords Science and Technology Committee is launching an inquiry into innovation in the NHS, with a focus on personalised medicine and AI.
- Call for evidence
- Innovation in the NHS: personalised medicine and AI
- Science and Technology Committee
Background
Advances in artificial intelligence and genomics offer the prospect of developing truly personalised medicine across prevention, diagnosis, and treatment.
Recent advances in genomics, AI-driven data analytics and biotechnology are enabling new therapies like CAR T-cell therapy, which uses modified versions of the body’s own immune system cells to fight cancer, and other gene therapies that are highly customized to patients. These therapies offer life-saving potential, but the need to tailor them to the individual can make them expensive to deploy.
Novel developments in AI, such as the development of Google’s AlphaGenome model, offer further hope that genomic medicine will advance as our ability to understand genetic effects on health improves and the data analysis for personalised medicines becomes possible to automate. There are broad ambitions to use AI more widely to advance medical science; for example, the Government’s AI for Science strategy has a target to “Use AI to accelerate drug discovery to develop trial-ready drugs within 100 days by 2030 and contribute to deploying new treatments faster.” But translating the cutting edge of medical science into routine patient delivery in the NHS remains a challenge.
Our inquiry and its priorities
The Committee’s inquiry will seek to use personalised medicine and AI as examples to examine why the NHS adoption of the UK’s cutting-edge life sciences innovations often fails, and what could be done to fix it.
Questions
The Committee will explore:
- The current state of the science underpinning personalised medicine, the major gaps and existing possibilities, including the role of AI in personalised medicine.
- What research infrastructure is needed to support the development of personalised medicine and AI in the UK.
- How effective the UK is in translating its life sciences strengths into validated personalised medicine and AI tools and what can be done to remain competitive in this field.
- How proven innovations might be deployed across the NHS, and what key systematic barriers prevent or delay this.
- If regulatory frameworks are appropriate and proportionate, and where they could be improved.
- Whether current appraisal and commissioning models are appropriate for personalised medicine.
- What the Government needs to do to strengthen feedback loops between medical research, the life sciences industry and the NHS.
A full list of questions can be found in the Call for Evidence (see link above).
Chair’s quote
Lord Mair CBE, Chair of the Committee said:
“Advances in AI and genomics are creating the prospect of truly personalised medicine across prevention, diagnosis and treatment. Our inquiry will use personalised medicine as a case study to explore a broader question: why does the NHS struggle to adopt the UK’s cutting-edge life sciences innovations, and what could be done to fix that?”
“As the NHS plans to harness new developments in genomics, AI, and personalised medicine, our inquiry will seek to establish the state of the science and technology in this area and understand where patients might benefit from near-term developments. “
“We will examine the gap between early-stage research, clinical trials and NHS-wide delivery, looking at blockages in the system slowing progress, including procurement processes, clinical pathways, and the role of regulators and professional bodies.”
“We will also examine how the fragmentation of the overall NHS structure contributes to the uneven deployment of innovation, how the costs of personalised treatments can be reduced, and the clinical academics and clinical trials infrastructure needed to rapidly deploy innovations within the NHS.”
Timeline
The Committee invites written contributions to its inquiry by by 23.59 on Monday 20 April 2026.
Further Information
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![Figure 1: Fine-tuning improves robustness and performance jointly. Each foundation model is shown before (open circle) and after (filled circle) fine-tuning. The x-axis is the average PathoROB robustness index over three datasets, where higher values indicate greater robustness; the y-axis is the normalized rank sum over the HEST, THUNDER and Patho-Bench benchmarks, rescaled to [0, 1] so that 1 corresponds to the best achievable performance.](https://www.pathologynews.com/wp-content/uploads/2020/07/embedding-figure-800x600-1.png)



