Security News > 2021 > June > AI-based diagnostic processes that safeguard data privacy

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.

"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. AI-based diagnostic processes support doctors.

To address this problem, an interdisciplinary team at TUM has worked with researchers at Imperial College London and the non-profit OpenMined to develop a unique combination of AI-based diagnostic processes for radiological image data that safeguard data privacy.

"For our algorithm we used federated learning, in which the deep learning algorithm is shared - and not the data. Our models were trained in the various hospitals using the local data and then returned to us. Thus, the data owners did not have to share their data and retained complete control," says first author Alexander Ziller, a researcher at the Institute of Radiology.

To ensure 'differential privacy' - i.e. to prevent individual patient data from being filtered out of the data records - the researchers used a third technique when training the algorithm.

Rickmer Braren, the deputy director of the Department of Diagnostic and Interventional Radiology notes: "It is often claimed that data protection and the utilization of data must always be in conflict. But we are now proving that this does not have to be true." The scientists add that their method can be applied to other medical data, and not just x-rays.

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