How does artificial intelligence in radiology improve efficiency and health outcomes?

Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement.

Automated summary

The potential benefits of AI in radiology include improving workflow efficiency, reducing reading time, minimizing doses, earlier disease detection, enhanced diagnostic accuracy, and personalized diagnostics. However, the market is still evolving, and there is limited understanding of AI's value and contribution to clinical practice.