Researchers in the USA and China have published a paper in Nature Medicine that finds artificial intelligence (A.I.) is as capable as an experienced physician's assistant when it comes to automatically diagnosing common childhood diseases. Data from 600,000 Chinese pediatric patient records, covering a 18 month time period, were analyzed to train the A.I and validate the framework. 101.6 million data points from 1,362,559 pediatric patient visits were used. Other A.I. systems use machine learning classifiers (MLCs) which allow them to excel at creating image-based diagnosis. Analysis of diverse and massive electronic health record (EHR) data remains challenging for typical A.I. systems. The A.I. system developed by the researchers is capable of automatically extracting data from natural language records, can utilize hypothetico-deductive reasoning that is used by physicians and is capable of unearthing associations that previous statistical methods have not found. Because the research was done in China, it was much easier to accumulate the data. A new "American A.I. Initiative" was signed into law to encourage federal agencies and universities to share data and create similar automated systems, but pooling health care data in America is much more complicated. The equipment systems aren't standardized in America and getting permission to collect and use patient data can be hard; even if it is anonymized. When tested on unlabeled data, the software could rival the performance of experienced physicians. It was more than 90 percent accurate at diagnosing asthma; the accuracy of physicians in the study ranged from 80 to 94 percent. In diagnosing gastrointestinal disease, the system was 87 percent accurate, compared with the physicians' accuracy of 82 to 90 percent. The system was highly accurate, the researchers said, and one day may assist doctors in diagnosing complex or rare conditions. Able to recognize patterns in data that humans could never identify on their own, neural networks can be enormously powerful in the right situation. But even experts have difficulty understanding why such networks make particular decisions and how they teach themselves. As a result, extensive testing is needed to reassure both doctors and patients that these systems are reliable.