Enabling the Development and Application of Artificial Intelligence in the NHS
Description: THE PROMISE OF AI IN HEALTHCARE
Artificial intelligence (AI) is a field of study which tries to get computers to behave in ways we would consider intelligent if those same behaviours were exhibited by humans - for example, the replication of human cognitive skills such as problem solving. But AI has huge potential beyond the mimicking of human behaviours - it is a fundamental technology that can allow meaningful processing of data beyond the comprehension of the human brain. The promise of AI for healthcare is thus clear - it could allow every diagnosis and treatment to be personalized on the basis of all known information about a patient, incorporating lessons from collective experience. By streamlining workflows, providing automated diagnosis of routine, non-serious conditions, and by allowing liberation from keyboards, AI could ultimately provide healthcare professionals the "gift of time" - the dedicated time really required to provide the best possible care for patients.
OBJECTIVE
Much of the exceptional recent progress in the application of AI to healthcare has come in the diagnosis of eye disease. This includes work that I initiated and led - the collaboration between Moorfields Eye Hospital and Google DeepMind to apply AI to retinal diseases such as age-related macular degeneration (AMD). My fundamental objective in this fellowship will be to drive the development and application of AI on the NHS using ophthalmology as a model. By emphasizing a central role for patients, and a thoughtful approach to use of their data, this fellowship will provide experience that can be shared with other medical specialties, helping the NHS become a world leader in AI.
TRAINING AND DEVELOPMENT
This fellowship will allow me to become a world leader in the application of AI to healthcare, developing the leadership and technical skills to lead a diverse and multi-disciplinary research group, establish collaborations, and drive innovation.
CASE FOR SUPPORT
The infrastructure component will focus on development of an ophthalmic bioresource that can be used to build AI systems and to evaluate their clinical performance. Using this, I will lead novel approaches to educating patients about how their data is used to develop AI systems.
The research component will begin by investigating the feasibility in healthcare of "AI that can build AI" - recently developed platforms that can allow healthcare professionals without any computer programming experience to explore AI. It will next focus on developing novel AI systems that can provide more individualized treatment of retinal diseases like AMD, can enhance and transform ophthalmic images to allow better diagnosis, and which can generate "imitation" ophthalmic images indistinguishable from real versions. Finally, the research component will focus on links between the eye and the rest of the body, using AI in an attempt to predict the future development of diseases such as Alzheimer's, stroke, and heart attack.
Finally, in years 5-7 of the fellowship, I plan to focus more on the practical implementation of AI systems in patient care pathways in the NHS and around the world.
POTENTIAL APPLICATIONS AND BENEFITS
This fellowship will benefit the academic community and industry by greatly facilitating the development of AI systems for ophthalmology. Through the creation of benchmark datasets, it will assist regulators to ensure the safety and effectiveness of AI systems before they are implemented in the real world. Most importantly, these systems will ultimately provide direct benefits for patients with better diagnosis and treatment of eye disease. The NHS will also benefit by allowing hospital eye services to become both more efficient and less expensive. Finally, this fellowship will benefit the public and policy makers by developing and disseminating best practice regarding the use of patient data in AI, as well as allowing them to visualize and contextualize its use.
Funder: Medical Research Council
Amount: £1,081,000
Funding type: fellowship
Funder scheme: UKRI FLF
Start date: 2020-07-31
End date: 2024-07-30
Funded outputs count: 21
Funder award ID: MR/T019050/1
OpenAlex ID: G8465227217