Source: healthitanalytics.com
To advance the use of machine learning in medical imaging, researchers will have to examine radiologists’ perceptions of the technology, as well as the cost-effectiveness of these tools, according to a study published in JMIR Medical Informatics.
Countless studies have shown the diagnostic accuracy of machine learning tools. Organizations across the care continuum, from research universities to companies like Google, have developed machine learning algorithms that can identify breast cancer in medical images as effectively as human clinicians.
While the results of these studies have promising implications for the future of radiology and pathology, JMIR researchers noted that more investigations may be necessary before machine learning tools can become part of routine clinical practice.
“Advancements in computer algorithms are becoming increasingly sophisticated and widespread in the field of radiology, with the potential to be cost-effective for increasing detection rates of various medical conditions and improve the efficiency of radiologists,” the team said.
“As we continue to head into an artificial intelligence era, it is essential that we understand the implementation of technologies in healthcare settings and its impact on health care providers and their potentially shifting roles.”
The group analyzed nine peer-reviewed articles that focused on the implementation and adoption of computer-aided detection (CAD) in breast cancer screening. CAD, a form of machine learning, can help clinicians interpret medical images by acting as a double check or a second pair of eyes, replacing the typical double reading by a second pathologist.
CAD scans digital mammograms and marks areas of potential cancer, which pathologists then review to reach a final assessment of the image. Although the use of CAD has increased significantly over the past several years, researchers stated that studies have largely overlooked radiologists’ perceptions of the technology, as well as its cost-effectiveness and efficiency.
After reviewing past articles, the team found that incentives for adopting CAD included improved cancer detection rates, breast imaging profitability, and less radiologist time taken.
However, researchers also found that providers didn’t have an overly positive view of the technology. In general, radiologists had more favorable perceptions of double reading by a colleague rather than single reading with CAD. One study showed that 74 percent of radiologists believed double reading improved cancer detection rates, while just 55 percent thought that CAD improved detection rates.
Additionally, the group found that the use of CAD was associated with higher interpretation times. CAD may take less time than double reading by a second radiologist, but researchers saw that when radiologists reviewed CAD-marked images, the mean interpretation time increased by 19 percent.
CAD implementation was also associated with a significant increase in recall rates, which occurs when a patient is called back for follow-up imaging. Moreover, the use of CAD for breast cancer screening can be associated with higher financial costs, depending on the accuracy of CAD, the number of patients screened, and comparison with single versus double reading.
These results indicate that more research is needed to identify and overcome barriers to machine learning adoption in the medical imaging field.
“Through our scoping review of the adoption and implementation of CAD in clinical settings for breast cancer detection and other related articles, CAD use by radiologists is based on trade-offs between the barriers and facilitators,” researchers said.
“The use of CAD for breast cancer screening involves several tradeoffs including weighing the impact on detection rates and patient outcomes, costs and financial incentives, time saved from double reading, increased recall rates, and radiologist perceptions.”
The study was limited in that researchers reviewed only a small number of articles. However, the results indicate that further research is needed to assess the implementation and adoption of machine learning in medical imaging.
“Our review suggests that there is a large focus on the diagnostic accuracy of CAD, but little focus on CAD implementation and perceptions of radiologists—the end users,” researchers said.
“We propose that further studies be carried out to better understand CAD adoption and implementation in clinical settings. Specifically, there should be a focus on investigating radiologists’ perceptions of CAD use in various settings, as we only came across one such study based in the United States, which cannot be generalized to other settings and health care systems.”