From the July 2019 issue of HealthCare Business News magazine
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Studies indicate that AI provides equivalent or better results than CAD for unaided interpretation by a radiologist while not adding to the reading time. This should help with the patient throughput, especially for busy imaging centers. One study reflected that sensitivity increased with AI from 83% to 86%, while specificity increased from 77% to 79%.
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Another study compared AI with the performance of 101 radiologists. Using the Area Under the Curve (AUC) as a guide, the AI system had an AUC higher than 61.4% of the radiologists.
One primary goal of AI is to reduce false positive per image (FPPI). A study published in the Journal of Digital Imaging in April 2019 showed that using the AI-based CAD, as compared to CAD, resulted in a 69% decrease in FPPI. This should translate to significant savings to the healthcare industry.
The two prime directives of value-based healthcare are improving outcomes and controlling costs. The total cost for follow-up diagnostic mammogram studies ranges from $150 to $300 (global fees under Medicare payment rules). Ultrasound ranges from $120 to $150 (global fees under Medicare payment rules). These represent a $4 billion per year market.
When cancer is suspected, the only definitive way of ruling it out is by performing a biopsy and examining the tissue under a microscope. The cost of minimally-invasive breast biopsy ranges from $1,500 to $2,500. This translates to an estimated breast biopsy market of over $3 billion per year. Considering 30% of all biopsies turn out negative, even a 2%–3% improvement in ruling out false positives offers the promise of better outcomes and millions in cost avoidance. Overall, it’s a win/win for the patient and the payer. And AI is still learning.
About the author: James Laskaris is the senior clinical strategist at MD Buyline.Back to HCB News
