A research paper has been published in Nature Communications by a team of Babylon scientists that has explored the use of machine learning in primary care.
The paper, titled ‘Improving the accuracy of medical diagnosis with causal machine learning’, explores the use of AI, believed to be a first , using the principles of causal reasoning to enable AI to diagnose written test cases.
The researchers used a new approach, known as causal machine learning, where the AI could consider what symptoms it might see if the patient had an illness different to the one it was considering.
Dr Jonathan Richens, Babylon scientist and lead author, said: “We took an AI with a powerful algorithm, and gave it the ability to imagine alternate realities and consider ‘would this symptom be present if it was a different disease’? This allows the AI to tease apart the potential causes of a patient’s illness and score more highly than over 70% of the doctors on these written test cases.”
Dr Ali Parsa, CEO and Founder, Babylon, said: “This should not be sensationalised as machines replacing doctors, because what is truly encouraging here is for us to finally get tools that allow us to increase the reach and productivity of our existing healthcare systems.”
“AI will be an important tool to help us all end the injustice in the uneven distribution of healthcare, and to make it more accessible and affordable.”
The AI written medical cases study
A pool of over 20 Babylon GPs created 1,671 realistic written medical cases – these included typical and atypical examples of symptoms for more than 350 illnesses.
Each case was authored by a single doctor and then verified by multiple other doctors to ensure it represented a realistic diagnostic case.
A separate group of 44 Babylon GPs were then each given at least 50 written cases (the mean was 159) to assess. The doctors listed the illnesses they considered most likely (on average returning 2.58 potential diseases for each diagnosis). They were measured for accuracy by the proportion of cases where they included the true disease in their diagnosis.
Dr Tejal Patel, Associate Medical Director and GP, Babylon, said: “I’m excited that one day soon this AI could help support me and other doctors reduce misdiagnosis, free up our time and help us focus on the patients who need care the most. I look forward to when this type of tool is standard, helping us enhance what we do.”
Dr Saurabh Johri, Chief Scientist and author, Babylon, added: “The algorithm performed particularly well for rare diseases which are more commonly misdiagnosed, and more often serious. Switching from using correlations improved accuracy for around 30% of both rare and very-rare conditions.”
