Machine learning is expected to greatly impact radiology in the coming years. However, the extent of the impact will depend on the practical utility of the machine learning tools that are being developed. Engineers and computer scientists have suggested that modern machine learning algorithms require only data and compute power to create models that can perform complex tasks. The definition of the task, however, has a considerable impact on the practical utility of the resulting model and product. We will explore what is needed to successfully deliver and deploy products based on modern machine learning that actually address clinical problems. What really is the task that a radiologist performs in breast cancer screening and what can machine learning practitioners and product managers learn from that and vice versa? This raises the intriguing question - what we can do to optimise how experts from various fields learn from each other to deliver products that positively impact doctors, the healthcare industry, and patients?
Breast cancer screening remains one of the most promising areas in medical imaging to deliver the impact of AI at scale. However, building a clinically robust solution deemed safe to deploy on diverse screening populations, that also generates meaningful outcomes for radiologists and patients, remains a challenge. Join Kheiron Medical Technologies and a panel of leading breast imaging experts, researchers and radiology leaders to discuss a framework for selecting and deploying safe and impactful AI into your screening programme.
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