Nearly half of the AI-based medical devices approved by the US Food and Drug Administration (FDA) have not been trained on real patient data, according to a new study. The study, published in Nature Medicine, finds that 226 of the 521 devices authorised by the FDA lack published clinical validation data. "Although AI device manufacturers boast of the credibility of their technology with FDA authorisation, clearance does not mean that the devices have been properly evaluated for clinical effectiveness using real patient data," says first author Sammy Chouffani El Fassi. The US team of researchers examined the FDA's official "Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices" database. "Using these hundreds of devices in this database, we wanted to determine what it really means for an AI medical device to be FDA-authorised," says Professor Gail Henderson, a researcher at the University of North Carolina's Department of Social Medicine. Of the 521 devices in this database, just 22 were validated using the "gold standard" " randomised controlled trials, while 43% (226) didn't have any published clinical validation. Some of these devices used "phantom images" instead " computer-generated images that didn't come from real patients. The rest of the devices used retrospective or prospective validation " tests based on patient data from the past or in real-time, respectively.