Machine learning is only as good as the quality of the training data – and the current vogue for deep learning networks requires very large datasets to prevent over-fitting. The only route to substantial progress is the development of accessible global registries that link image data with diagnoses and outcomes. As a community of healthcare users and providers we need to accept that the benefits of well-managed data sharing vastly outweigh the potential risks if there is robust protection of confidentiality and safeguards to prevent restrictive commercial exploitation. International standards for the validation of AI in medicine must be established and be a requirement for publication. The American College of Radiology should be congratulated for establishing a Data Science Institute to guide the appropriate development and implementation of AI tools to help radiologists improve medical imaging care.
Autopilots have not replaced humans and have been instrumental in improving the precision, economy and safety of air travel. The same might be expected for the use of automation in medical imaging if appropriate safeguards are in place. Instead of facing extinction, radiologists will be pivotal in this information revolution. If the evidence base is sound we owe it to our patients to embrace new AI technology and be pioneers of its adoption in healthcare.