Sharing patient data by applying machine learning in hospitals

Federated learning is a new and promising approach to make the adoption of machine learning and patient data sharing possible in the medical field.

Despite the widely popular application of AI and Machine Learning across industries, the healthcare sector always hesitates to embrace the technology of the future due to privacy problems. The concern for patient confidentiality has always been the dilemma impeding the adoption of machine learning or the potentials and the power to transform the healthcare landscape.

Sharing patient data by applying machine learning (Photo: HealthCatalyst)

Federated learning comes as the solutions for the dilemma as the technology generates almost identical results as the method that is not protected does. Federated learning was first used by Google to train an algorithm through several decentralized computers containing local data samples, without sharing them. And the method shows great potentials in brain imaging,  analyzing magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.

For instance, doctors in different parts of the world can enter their patient scan data, training on a shared model, then the new model will be transferred to a centralized one. By doing that, the model has gained the knowledge front he hospital and generate useful clinical data.  "Traditionally, machine learning has used data from a single institution, and then it became apparent that those models do not perform or generalize well on data from other institutions.", said Spyridon Bakas.

In regard to tumor boundaries, Bakas the opinion not only varies from person to person but also differs from one day to another of the same doctor. "Artificial Intelligence allows a physician to have more precise information about where a tumor ends, which directly affects a patient's treatment and prognosis.". He said.

In the research to study the effectiveness of the new technology, the outcomes that have been produced by federated learning are little to no significant differences compared to the other methods. The technology opens a new promising field that needs more study, expanding the application of the use cases and industries other than healthcare.

Source: Science Daily.

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