Philips to develop AI healthcare solutions for UK life sciences

Health technology company Philips has responded to the government’s Industrial Strategy Challenge fund.

The Fund, which will invest an additional £4.7 billion into research and development over the next four years, is part of the government’s Industrial Strategy. The fund aims to help develop digital pathology programs that use artificial intelligence.

Philips will work with those involved in the Industrial Strategy, such as public bodies and the Office of Life Sciences to develop AI and digital diagnostic solutions.

The company is currently working with three NHS Sites in Scotland to find out how networked digital pathology services can improve patient outcomes in remote areas. If the trials are successful then the model could be rolled out nationally.

Philips’ recent acquisition of the digital pathology software company PathXL in Northern Ireland has resulted in its workforce doubling within 12 months

Philips Electronics UK&I CEO Neil Mesher, said: “Philips is proud to support this programme. We’re excited to be contributing to the possible development of a new global industry that utilises AI and machine learning. Philips believes that health knows no bounds and a campaign like this, which explores possibilities of accelerating diagnosis and improving  accuracy, could have profound implications in disease areas such as cancer where precision diagnosis can be critical to patient survival.”

Professor Manuel Salto-Tellez clinical professor, director of the Precision Medicine Centre of Excellence in Queen’s University Belfast, said: “Pathology is involved in 70% of the diagnoses made within the NHS. In the UK, hospital treatments for over 75s have increased by 65% in the past decade, growing the demand for pathology services. At Queen’s we carry out leading digital pathology research to help meet this growing need. We believe digitised pathology can help to efficiently improve patient care by reducing human errors associated with sample handling, minimising variations in the clinical interpretation of data, and offering tools for increasingly sophisticated sample analysis.”



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