London-based Scalpel AI, combining computer vision and machine learning to identify and track surgical instruments, has raised £3.8 million in a funding round designed to help the company scale its global operations and roll out its technology across the healthcare supply chain.
Scalpel AI’s tech generates a digital twin for each surgical instrument as the tool is moved throughout its journey, and can also verify that surgical trays contain the right equipment when they are delivered to the operating theatre. Through validating and tracking surgical tools, Scalpel AI aims to enhance patient safety, reduce equipment loses and improve visibility, as well as tackling “costly mistakes”.
The funding round was led by Mercia Ventures, with participation from Tensor Ventures and private investors.
AI wider trend
Last week we took a look at some recent uses of artificial intelligence across the NHS, from supporting diagnosis to personalising treatment to predicting disease.
We explored guidance from NHS England on evaluating AI projects and technologies, focusing on eight key domains: safety, accuracy, effectiveness, value, whether the tech addressed requirements and population needs at the site at which it was deployed, the reasons for implementation and barriers faced, feasibility for scaling up, and sustainability.
Recent news from University Hospitals Coventry and Warwickshire highlighted how AI is being utilised to tackle waiting lists and improve patient experience.
We reported how the UK government has awarded £12 million in funding for projects utilising innovative technologies such as AI, VR and wearable sensors in supporting people with drug addictions and reducing drug-related deaths.
HTN noted how DeepHealth, provider of a portfolio of AI solutions designed to support breast, lung, prostate and neurological care, has acquired cancer diagnostic company Kheiron Medical Technologies Limited as part of efforts to expand its portfolio of AI-powered diagnostic and screening solutions.
And we hosted a panel discussion exploring whether the reality of AI will live up to current hype, as well as exploring how bias in healthcare data can be managed; you can catch up with the key points of the discussion here.
