UKRI announces £13 million funding for artificial intelligence in health, with three projects launched at Imperial College London. Imperial College London researchers have been awarded almost £1.8m as part of a new stream of funding from UK Research and Innovation (UKRI) to explore how artificial intelligence (AI) can improve health research.
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IMAGE: PD Dr. Rickmer Braren (l.) und Prof. Daniel Rueckert (r.) exploring diagnostic possibilities using artificial intelligence for medical image data. view more
Credit: Andreas Heddergott / TUM
Digital medicine is opening up entirely new possibilities. For example, it can detect tumors at an early stage. But the effectiveness of new AI algorithms depends on the quantity and quality of the data used to train them.
To maximize the data pool, it is customary to share patient data between clinics by sending copies of databases to the clinics where the algorithm is being trained. For data protection purposes, the material usually undergoes anonymization and pseudonymization processes - a procedure that has also come in for criticism. These processes have often proven inadequate in terms of protecting patients health data, says Daniel Rueckert, Alexander von Humboldt Professor of Artificial Intelligence in Healthcare and Medicine at TUM.
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New AI technology protects privacy in healthcare settings
Researchers at TUM and Imperial have developed a technology that protects patients’ personal data while training healthcare algorithms.
The technology has now been used for the first time in an algorithm that identifies pneumonia in x-ray images of children. The researchers found that their new privacy-protecting techniques showed comparable or better accuracy in diagnosing various pneumonias in children than existing algorithms might.
Guaranteeing the privacy and security of healthcare data is crucial for the development and deployment of large-scale machine learning models. Professor Daniel Rueckert Department of Computing
Artificially intelligent (AI) algorithms can support clinicians in diagnosing illnesses like cancers and sepsis. The effectiveness of these algorithms depends on the quantity and quality of the medical data used to train them, and patient data is often shared between clinics to maximise th