Care Quality Commission
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Using machine learning in diagnostic servicesUsing machine learning in diagnostic services

We yesterday published the findings of our second regulatory sandboxing pilot. The pilot focused on the use of machine learning applications in diagnostic services.

Machine learning can be used for a wide range of diagnostic data and could contribute to identifying disease or clinical risks to people’s health faster and more accurately. The applications can support healthcare staff when they use medical imaging to help diagnose a wide variety of conditions. For example, by helping to interpret X-rays, physiological measurement, and other clinical data.

The sandbox pilot aimed to identify what is needed to deliver high-quality care in services that use these applications and the risks involved. We therefore worked with healthcare providers, technology suppliers, people who use services, clinicians, and other stakeholders to do this.

The report published yesterday (Wednesday 25 March) sets out our findings.

Nigel Acheson, CQC’s Deputy Chief Inspector of Hospitals, yesterday said:

“Part of CQC's purpose is to encourage improvement, while making sure that safety is at the heart of care. As new ways of working develop, we want to support innovation and help providers to reach the best outcome for people using services.”

CQC’s regulatory sandboxing is part of our work to encourage innovation, quality and safety. It is supported by the Department of Business, Energy and Industrial Strategy’s (BEIS) Regulators’ Pioneer Fund. It involves working proactively and collaboratively to understand new types of health and social care service, agree what good quality looks like, and develop our approach to regulation. We think this is particularly important for innovative and technology-enabled services, which are developing quickly.

For background

Machine learning is a set of software algorithms and statistical models that computer systems use to perform a specific task, without using explicit instructions. This approach is different from other types of software where coding is done intentionally and transparently, based on what developers know. The government identified machine learning and other artificial intelligence techniques as a breakthrough technology, which can bring big benefits to services and the people that use them.


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