International researchers developed an artificial intelligence (AI) model using 101.6 million data points from electronic health records in China. The AI diagnoses were comparable to examining physicians, and it was able to distinguish between common conditions and life-threatening conditions with a high level of accuracy, say the authors.
An AI model with high accuracy, comparable to that of experienced pediatricians in diagnosing common childhood diseases, is reported in a paper published online this week in Nature Medicine.
Kang Zhang and colleagues developed an AI-based model that applies an automated natural language-processing system that uses deep learning techniques to identify clinically relevant information from electronic health records. This model can search electronic health records and unearth associations that previous statistic methods have not found. In total, 101.6 million data points from 1,362,559 pediatric patient visits to a major referral centre in Guangzhou, China, were analysed to train and validate the framework.
The model demonstrated a high level of accuracy for diagnoses compared with initial diagnoses by an examining physician. It also performed well when diagnosing two important categories of disease: common conditions, such as influenza and hand-foot-mouth disease, and dangerous or life-threatening conditions, such as acute asthma attack and meningitis.
The authors conclude that this type of AI framework may be useful for streamlining patient care, such as by triaging patients and differentiating between those patients who are likely to have a common cold from those who need urgent intervention for a more serious condition.