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Healthcare costs continue to rise, clinicians are overworked, and patient data privacy, security, and compliance are ongoing concerns. With limited budgets and shrinking margins, healthcare organizations must find new ways to improve operational efficiency while meeting—or exceeding—the highest standards of patient care. According to a Deloitte 2019 Global Healthcare Outlook report, expenditures on healthcare services are expected to increase at an annual rate of 5.4% between 2017 and 2022—from $7.7 trillion to $10 trillion.

Healthcare organizations are looking to AI to improve efficiencies and reduce costs. From medical imaging to robot-assisted surgery to drug discovery, AI is getting better and more sophisticated at doing what humans do—and doing it more accurately, faster, and at lower cost. According to the Accenture report “Artificial Intelligence (AI): Healthcare’s New Nervous System,” by 2026 AI is expected to create up to $150 billion in annual savings for the healthcare industry.

With the growing focus on early intervention, preventive healthcare, and digital transformation, healthcare organizations are increasing their adoption of medical imaging technologies. Advances in these technologies, including 3D and 4D capabilities, real-time analytics, and processing accelerated by graphics processing units (GPUs), give radiologists powerful tools to make faster and more accurate diagnoses and help to prevent radiologist burnout.

Improved Diagnostics

Many cancers start with changes so small that no human can detect them, even with current medical imaging technology. However, AI programs can be trained with deep learning to see the very earliest changes in cell structure that typically develop into cancerous cells. These programs can alert oncologists, who can then guide patient care protocols with greater accuracy and effectiveness. For example, the use of AI is reducing diagnostic errors in breast cancer detection by 85%.

Preventing Radiologist Burnout

Modern imaging technologies generate an overwhelming amount of information that can be difficult and time consuming for radiologists to process manually. Specialized AI applications can support radiologists and prevent burnout by “triaging” stacks of images. By quickly sorting out normal images and flagging exceptions, the radiologist can spot the images that show anomalies or indicators of disease and focus on diagnosing and treating the disease instead of screening images. For example, AI enables MRIs to accelerate image reconstruction by 100 times, and with 5 times greater accuracy.