ChatGPT Bombs Test On Diagnosing Kids' Medical Cases With 83% Error Rate

An anonymous reader quotes a report from Ars Technica: ChatGPT is still no House, MD. While the chatty AI bot has previously underwhelmed with its attempts to diagnose challenging medical cases -- with an accuracy rate of 39 percent in an analysis last year -- a study out this week in JAMA Pediatrics suggests the fourth version of the large language model is especially bad with kids. It had an accuracy rate of just 17 percent when diagnosing pediatric medical cases. The low success rate suggests human pediatricians won't be out of jobs any time soon, in case that was a concern. As the authors put it: "[T]his study underscores the invaluable role that clinical experience holds." But it also identifies the critical weaknesses that led to ChatGPT's high error rate and ways to transform it into a useful tool in clinical care. With so much interest and experimentation with AI chatbots, many pediatricians and other doctors see their integration into clinical care as inevitable. [...] For ChatGPT's test, the researchers pasted the relevant text of the medical cases into the prompt, and then two qualified physician-researchers scored the AI-generated answers as correct, incorrect, or "did not fully capture the diagnosis." In the latter case, ChatGPT came up with a clinically related condition that was too broad or unspecific to be considered the correct diagnosis. For instance, ChatGPT diagnosed one child's case as caused by a branchial cleft cyst -- a lump in the neck or below the collarbone -- when the correct diagnosis was Branchio-oto-renal syndrome, a genetic condition that causes the abnormal development of tissue in the neck, and malformations in the ears and kidneys. One of the signs of the condition is the formation of branchial cleft cysts. Overall, ChatGPT got the right answer in just 17 of the 100 cases. It was plainly wrong in 72 cases, and did not fully capture the diagnosis of the remaining 11 cases. Among the 83 wrong diagnoses, 47 (57 percent) were in the same organ system. Among the failures, researchers noted that ChatGPT appeared to struggle with spotting known relationships between conditions that an experienced physician would hopefully pick up on. For example, it didn't make the connection between autism and scurvy (Vitamin C deficiency) in one medical case. Neuropsychiatric conditions, such as autism, can lead to restricted diets, and that in turn can lead to vitamin deficiencies. As such, neuropsychiatric conditions are notable risk factors for the development of vitamin deficiencies in kids living in high-income countries, and clinicians should be on the lookout for them. ChatGPT, meanwhile, came up with the diagnosis of a rare autoimmune condition. Though the chatbot struggled in this test, the researchers suggest it could improve by being specifically and selectively trained on accurate and trustworthy medical literature -- not stuff on the Internet, which can include inaccurate information and misinformation. They also suggest chatbots could improve with more real-time access to medical data, allowing the models to refine their accuracy, described as "tuning."

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