Building an AI Platform for Medical Imaging System

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AI can help doctors spot anomalies in patient scans, but building a real-world AI medical imaging system at scale faces significant challenges.

eetasia.com, LONDON, Jul. 01, 2019 – 

A new project is set to build the first AI platform for medical imaging, to be used by the UK's National Health Service (NHS). The project, which will be run by King's College London (KCL) in collaboration with Nvidia, aims to automate the interpretation of radiology data, the most time-consuming parts of the radiology process.

While AI and machine learning have long been touted as an enabling technology for medical imaging analysis and diagnostics, implementing such a system in the real world is significantly harder than the rapid pace of technology advances would suggest.

"A key challenge will be perception, within the clinical community and the public," said M. Jorge Cardoso, CTO of the London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare at KCL.

The AI system will not make patient care decisions, but will support clinical teams to ensure the right patients are prioritized, and provide supporting information to assist in decision-making.

"It is our responsibility to ensure that this [system] is seen as making radiologists more effective, supporting their decision-making process and ultimately improving patient care and patient throughput through the hospital," Cardoso said.

Patient Confidentiality

Another big challenge of building a system like this is the need for patient confidentiality. For the first time, the NHS will use federated learning techniques to tackle patient privacy and data governance issues.

Federated learning "outright avoids many of the classical issues of privacy in healthcare," said Cardoso. "The key goal is to not move the clinical data from where it currently rests – this means that it stays within the hospitals' protection, under its governance and security procedures."

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