Implementing AI in breast imaging: challenges to turn the gadget into gain

In healthcare, the implementation of artificial intelligence (AI) is rapidly gaining momentum with breast imaging being no exception, or rather a poster child case. Numerous clinical indications intuitively lend themselves to AI enhancement. While the adoption of broad AI in clinical breast imaging practice has been more or less a silent revolution – it is already widely used for invite coordination, appointment scheduling, report generation, image positioning, and quality insurance – the integration of narrow AI in clinical routine is having a harder time. The hurdles and challenges are mainly related to, but not limited to, data availability and quality, algorithm development, evaluation, validation and explainability, user education, and lego-ethical issues.

We as a community agree that to overcome the current barriers and to facilitate the speedy implementation of AI in breast imaging, high-quality and diverse data, adequate means for data sharing, and multivariable algorithm development within multi-site/international collaborations are necessary. In addition, adequate user education, the establishment/adoption of performance benchmarks as well as lego-ethical frameworks for AI-enhanced breast imaging are required. Another factor, which is probably the largest barrier to the rapid implementation of AI in breast imaging, is the associated substantial costs, or rather the lack of existing financial resources.

In the end, its implementation does not so much come down to our faith in AI, our fear of being replaced by a machine, data sharing, or other challenges, but the good old problem of the cost of turning a gadget into gain.

Article: Implementing AI in breast imaging: challenges to turn the gadget into gain

Author: Katja Pinker

WRITTEN BY

  • Katja Pinker

    Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service

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