Computed tomography, X-ray radiography, and magnetic resonance imaging have been invaluable tools in disease diagnosis and treatment efficacy assessments, but the practice of manual viewing and grading medical images to identify abnormalities is time-consuming and can delay diagnosis for critical patients. Intelligent imaging analysis systems that use artificial intelligence, deep learning, and machine learning algorithms are gaining traction because they can quickly scan through huge volumes of medical images to detect abnormalities. The industry for intelligent imaging analysis systems for automated identification, decision support, and diagnosis is poised for strong growth. They allow healthcare providers to keep pace with the increasing volume of diagnostic imaging procedures despite a shortage of radiologists and trained clinicians.

• What best practices can organizations adopt to leverage intelligent imaging analysis systems for automated identification?
• In what ways can intelligent imaging systems transform diagnostic workflows amid radiologist and clinician shortages?
• Which top companies are driving innovation to pave the way for growth with automated identification and decision support?