Consulting giant Accenture predicts explosive growth in the health AI market, from $600 million in 2014 to a predicted $6.6 billion in 2021 – a remarkable 40 percent compound annual growth rate. Furthermore, research shows that most consumers are ready to accept and embrace AI for healthcare needs. As a result, investment is pouring into this segment.
There is a powerful case for the adoption of AI – or machine learning, or deep learning – for medical imaging. It is a data-rich and complex clinical specialty which lends itself to intelligent, automated decision-making based on pixel data and pattern recognition.The sheer volume and complexity of data from X-rays, MRIs, CAT scans, and other imaging modalities, sometimes in data-heavy 3D and often in combination for a single patient, is daunting. It is estimated that medical imaging data comprises 90 percent of all healthcare data – and yet the vast majority of that data is not used or analyzed. Turning these petabytes of data into useful information is the promise of AI.Unique AI algorithms can identify abnormalities with lightning speed and enhanced precision, assisting experienced clinical professionals in making more accurate diagnoses, more quickly. Additionally, the ability to automate more routine aspects of image analysis frees clinical experts to add more human value. Accenture has identified “Automated Image Diagnosis” as one of 10 AI applications with the greatest near-term impact for cost savings potential in healthcare.In addition to clinical applications, AI contributes to increased efficiency in areas such as workflow automation. For example, automated case assignment combines real-time variables with historic reading performance data to predict workload capacity and projected read times, helping organizations maximize capacity while improving the quality of interpretation.
The American College of Radiology has embraced the promise of AI and is working to accelerate AI adoption within medical imaging. The organization’s Data Science Institute™ has developed and released a collaborative framework of AI use cases to promote standardization, interoperability, reportability, and patient safety as we enter a new era of advanced medicine.
It is time to harness the promise of AI.