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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Date Time
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
25 May 2021
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 the data pool.