With artificial intelligence (AI) precipitously perched at the apex of the hype curve, the promise of transforming the disparate fields of healthcare, finance, journalism, and security and law enforcement, among others, is enormous. For healthcare – particularly radiology – AI is anticipated to facilitate improved diagnostics, workflow, and therapeutic planning and monitoring. And, while it is also causing some trepidation among radiologists regarding its uncertain impact on the demand and training of our current and future workforce, most of us welcome the potential to harness AI for transformative improvements in our ability to diagnose disease more accurately and earlier in the populations we serve.
Highlights
- As in the case of most disruptive technologies, assessment of and consensus on the possible ethical pitfalls lag.
- selection bias in AI datasets can result in inaccurate results in under-represented patient groups.
- AI models with imaging data acquired from one setting may poorly generalize to other practice settings.
- Patient image data ownership regulations vary by country and domain.
De-identification of image data used in AI algorithms may inadvertently reveal protected health information.
Authors: Nabile M. Safdar, John D. Banja, Carolyn C. Meltzer