AI and healthcare professionals are warning of concerns over a machine learning algorithm made for diagnosing COVID-19. The idea behind these technologies was to help health professionals tell the difference between coronavirus and other similarly-presenting ailments like pneumonia. But concerned professionals say changes need to be made before the COVID diagnosis machine learning is used in a clinical environment. Of those 62 papers included in the analysis, roughly half made no attempt to perform external validation of training data, did not assess model sensitivity or robustness, and did not report the demographics of people represented in training data. "Frankenstein" datasets, the kind made with duplicate images obtained from other datasets, were also found to be a common problem, and only one in five COVID-19 diagnosis or prognosis models shared their code so others can reproduce results claimed in literature.

To get a more accurate assessment of the opinion of leading researchers in the field, I turned to the Fellows of the American Association for Artificial Intelligence, a group of researchers who are recognized as having made significant, sustained contributions to the field. "In his book, Nick Bostrom has defined Superintelligence as'an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.' When do you think we will achieve Superintelligence?"