AI in radiology: top 10 use cases and best practices

While input from field experts is always critical to the development of AI solutions in virtually any industry, medical imaging calls for radiologists to have a much more decisive and significant role in the development of AI-based radiology solutions. Engineers and data scientists lack an understanding of essential radiological and anatomical concepts, which is crucial to creating AI systems applicable in radiology. On top of that, radiology is a very nuanced medical field, where therapeutic methods tend to change every few years. It’s highly advisable to involve radiologists in every phase of development.

Healthcare providers should get key decision-makers and executive leadership on board with AI implementation. According to the latest report by Deloitte, healthcare providers who succeeded in AI implementation put leaders at the core of AI transformation. These leaders should clearly communicate with the radiology department on how new tools will impact their daily workflows.

In a recent study, radiologists received chest X-rays and diagnostic advice from human experts and from AI systems. The radiologists rated AI advice as lower quality, which highlights the persistent 'AI black box' problem. In healthcare, where decisions can directly influence one's well-being, it is paramount for radiologists to understand the underlying decision-making of AI systems. Most AI radiology tools use deep learning models, and system interpretability and transparency become crucial. While deciphering deep learning models is a universal task across many AI applications, developers are responsible for minimizing bias and making models as explainable as possible. It comes down to continuous algorithm validation, dataset audit, evaluation of performance, standardized documentation, and routine bias assessment.