An AI (artificial intelligence) system designed to diagnose childhood diseases can recognise symptoms more accurately than many human doctors, study has shown.
The “deep learning” programme, tested in China, assimilated information from more than 1.4 million electronic health records.
It was then able to draw on its “experience” to diagnose a broad range of childhood diseases, with accuracy rates of more than 90% in some cases.
The system performed better than junior doctors and not quite as well as more senior experienced physicians.
The scientists who created the AI model believe it could speed up the triaging of patients in hospital emergency departments and improve the diagnosis of complex and rare conditions.
But sceptical British experts insisted that intelligent machines could never take the place of human physicians.
The human doctor who asked targeted questions and then used his or her knowledge and experience to make a diagnosis “can be considered a classifier of sorts”, said the researchers, writing in the journal Nature Medicine.
The AI programme worked in a similar way, sifting through vast amounts of clinical information “to mimic the clinical reasoning of human physicians”.
To create the system the scientists obtained electronic health records (EHRs) from 1,362,559 outpatient visits to the Guangzhou Women and Children’s Medical Centre, a major government hospital in China.
The records covered physician-patient encounters from January 2016 and July 2017 involving children and teenagers up to 18 years old.
In total, 101.6 million data points were used to “train” the programme.
The model was used to diagnose a host of common and dangerous conditions ranging from influenza and stomach infections to bacterial meningitis.
Specific words and phrases, as well as numerical data such as patient temperature, were analysed by the system and compared with what it had learned from the health records.
For instance, the programme identified “abdominal pain” and “vomiting” as being key symptoms associated with gastroenteritis.
Matching the system against the diagnostic records of 20 human physicians showed that it performed better than junior doctors and slightly worse than experienced specialists.
The researchers, led by Dr Kang Zhang, from the University of California at San Francisco, US, and Guangzhou Medical University, China, concluded: “Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity.”
British expert Professor Duc Pham, from the University of Birmingham, said the study was an “excellent application of deep learning” but pointed out that such systems could not guarantee 100% correct results no matter how much training they received.
He added: “Although the authors’ results show that on average their system performed better than junior doctors, it will not replace clinicians.
“Critical judgments or decisions must always be left to qualified human experts to make.”
Dr Paul Tiffin, an expert in adolescent psychiatry from the University of York, said: “The authors have shown the potential for machine learning to help support rapid diagnosis of illnesses in children.
“However, it should be stressed that the machine learning system used still relied on the quality of the recording of symptoms and other findings by clinicians.
“Thus, human health practitioners are not likely to be made redundant any time soon.”