Advances in imaging: Exploring the potential of artificial intelligence in radiology

Abstract

Radiology has seen significant advances in its quest to move from descriptive to diagnostic, and more recently to predictive. The long-standing reliance on traditional 2D X-rays has evolved into the application of high-powered magnets and CT scanners, which provide improved tissue contrast and 3D capabilities. These advances have led to more accurate identification of diagnostic imaging markers and increased the ability to identify biomarkers for diagnostic and predictive imaging. While these advances still fall within the realm of human interpretation, there is continued momentum to accelerate efforts that are reshaping the clinical workplace. Whether it is the need to harness the vast and largely untapped potential of artificial intelligence, the need to increase efficiency and reduce reading times, or the call to creatively improve patient care by integrating imaging and biomarker-related technologies, the community is pushing the discipline to evolve further. To help bring these necessary tools to clinical applications, collaborative efforts between clinicians, data scientists, and engineers will need to come together. The scientific community must work together to enable the clinical translation of technologies that combine diverse physical and biological imaging findings.