Researchers led by Dr Jakob Nikolas Kather, Visiting Associate Professor at Leeds’ School of Medicine, have developed a new way of using artificial intelligence to predict cancer from patient data without ‘putting personal information at risk’.
The new method uses ‘swarm learning’, a process which trains AI algorithms to detect patterns in data – with the algorithm updated to a central system, along with algorithms by other hospitals – to create an optimised algorithm that can then be reapplied back to the hospitals to improve sensitive detection capabilities. It means the process does not send any local or patient data, and the algorithm can continuously improve.
The research team trained the algorithms on study data from patients in Northern Ireland, Germany and the USA, and tested the algorithms on two large sets of data images generated at Leeds.
“We have shown that swarm learning can be used in medicine to train independent AI algorithms for any image analysis task,” said Phil Quirke, Professor of Pathology at Leeds. “This means it is possible to overcome the need for data transfer without institutions having to relinquish secure control of their data – creating an AI system which can perform this task improves our ability to apply AI in the future.”
Dr Kather added: “Based on data from over 5,000 patients, we were able to show that AI models trained with swarm learning can predict clinically relevant genetic changes directly from images of tissue from colon tumours.”
In March 2022, the NHS AI Lab published a blueprint for artificial intelligence validation, and highlighted a proof-of-concept validation process for testing the quality of AI in the health and care sector.
