“Radiology is evolving from a largely descriptive field to a more quantitative discipline. Intelligent software tools that combine quantitative imaging and clinical workflow features will not only enhance radiologist productivity, but also improve diagnostic accuracy,” said Simon Harris, Principal Analyst at Signify Research, and author of the market report “Machine Learning in Medical Imaging – 2017 Edition.”
The move to computer-aided software comes at a critical time for the field. “Many radiologists are working at full capacity,” the report noted. “The situation will likely get worse, as imaging volumes are increasing at a faster rate than new radiologists entering the field.”
And staffing challenges are not the only issue. “Radiologists are under increasing pressure due to declining reimbursement rates and the transition from volume-based to value-based care delivery,” it added.
The push to automated image-analysis got a boost in June, 2016, when IBM announced the formation of a Watson Health medical imaging collaborative, with a number of leading health systems, academic medical centers, ambulatory radiology providers and imaging technology companies.
“There is strong potential for systems like Watson to help to make radiologists more productive, diagnoses more accurate, decisions more sound, and costs more manageable,” said Nadim Michel Daher, a medical imaging and informatics analyst for Frost & Sullivan. “This is the type of collaborative initiative needed to produce the real-world evidence and examples to advance the field of medical imaging and address patient care needs across large and growing disease states.”
Watson has already shown promising results when matched against human diagnosticians.
In December, 2016, at the San Antonio Breast Cancer Symposium, researchers from India reported on findings that showed that Watson for Oncology (WFO) had shown “a high degree of concordance with the recommendations of a panel of oncologists in a double-blinded validation study.”
WFO analyzed the cases and came up with three recommendations – standard treatment (REC); for consideration (FC); and not recommended (NREC).
The study’s lead author, Dr. S.P. Somashekhar, chairman of the Manipal Comprehensive Cancer Center of Manipal Hospitals, in Bengaluru, India, told the audience at the meeting that the study found that the oncologists agreed with WFO about REC and FC recommendations 90 percent of the time.
The type — and complexity — of cancer impacted the rate of agreement. For non-metastatic disease, doctors and machine agreed 80 percent of the time. When metastases occurred, however, agreement was just 45 percent. WFO and physicians concurred in cases of triple-negative breast cancer 68 percent of the time.
More recently, a regional facility in South Florida became the first community medical center in the U.S. to adopt the cognitive computing platform in an effort to boost outcomes of patients afflicted with cancer.
“IBM’s technology, Watson for Oncology, will be provided to oncologists at Jupiter Medical Center to help doctors make personalized, evidence-based treatment decisions for their patients,” Dr. Andrew Norden, deputy chief health officer, IBM Watson Health, told HCB News. “For IBM, this is an important milestone, as it is the first adoption of Watson for Oncology to support community oncologists in the U.S.”
The advances in AI-based medical care are coming rapidly, and the roll-out has begun. “Deep learning is a truly transformative technology, and the longer-term impact on the radiology market should not be underestimated,” advised Signify’s Harris. “It’s more a question of when, not if, machine learning will be routinely used in imaging diagnosis.”