The Artificial Intelligence in Healthcare paper, commissioned by NHS Digital, looked at the clinical, ethical and practical concerns surrounding AI in the health and social care system in the UK.
The Academy identified seven key recommendations for politicians, policy makers and service providers to follow.
This included that such figures and organisations “should avoid thinking AI is going to solve all the problems the health and care systems across the UK are facing”.
Also the report said that claims AI can replace specialist clinicians are unlikely, but future doctors may also require training in data science.
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Professor Carrie MacEwan, chair of the Academy, said: “With AI starting to be used in some areas of clinical activity, such as retinal eye scans and targeting radiotherapy to treat cancer, it has been claimed the need for some medical specialists will be much reduced in the future.
“The Academy enquiry has found that, if anything, the opposite is true and that AI is not likely to replace clinicians for the foreseeable future but makes the case for training more doctors in data science as well as medicine.”
Other key recommendations included; AI must be developed in a regulated way between clinicians and computer scientists to ensure patient safety; clearer guidance around accountability, responsibility and wider legal implications of AI; data should be more easily available across private and public sectors for those who meet governance standards; and transparency of tech companies in order for clinicians to be confident in the tools they are using.
Dr Indra Joshi, digital health and AI clinical lead at NHS England, said: “We’ve got a real opportunity with AI based tech to gain time and efficiencies, but it has to be implemented in a safe and trusted way.”
AI is already used in the health sector in some capacity.
Google’s DeepMind has taught machines to read retinal scans with at least as much accuracy as an experienced junior doctor.
Other projects are underway, including a British Heart Foundation funded project to develop a machine learning tool that helps predict people’s risk of heart attack based on their health records.
Date: February 12, 2019
Source: Digital Health
