Experts say AI can lend a helping hand—but radiologists must learn to adapt
In a recent paper from consulting firm Deloitte, experts argue that evolving digital technology—notably artificial intelligence (AI)—has the potential to create jobs in many areas of healthcare, including diagnostic radiology.
AI has potential, but organizations are apprehensive about changes associated with the technology. But paper authors contend radiologists need to combine talent and technology to “join forces,” that than competing with one another. But the industry could be slow to embrace new styles of working.
For example, a 2017 study from Deloitte found, although 100 percent of healthcare providers surveyed planned to make significant progress in adopting cognitive and AI technologies in the next three to five years, none have made significant progress in doing so, according to Jeff Schwartz, the principal of human capital at Deloitte, and colleagues.
The group pointed to radiology as one area that could benefit from AI by reducing administrative tasks and boosting patient interaction.
“Diagnostic radiology is a prime area for change because it is plagued by burnout and turnover, is technological at its foundation, has a high volume of repetitive activities, and often does not require the radiologist to be at the same location as the patient,” Schwartz et al. wrote.
Authors listed three radiology “pain points” as areas ready for change:
- Radiological interpretation: “The interpretation of images is a core activity of diagnostic radiologists. Radiologists are increasingly having to complete a higher volume of studies in a shorter amount of time and interpret more images in each study.”
- Limited autonomy and uneven work distribution: “The anticipated consolidation of the radiology players in the market will likely lead to radiology groups growing larger and covering more facilities in more expansive areas. Consequently, radiologists may experience greater loss of autonomy and uneven work distribution.”
- Declining reimbursements and limited patient interaction: “Radiologists have a high burnout rate of 49 percent. Contributing to the burnout rate are the declining reimbursements which leads to longer hours, liability pressures, and the often sedentary and isolated nature of the work. Additionally, current radiology practice only involves a minor amount of patient interaction.”
Machine learning methods such as computer-aided detection have the potential to aid image analysis and can further cut read times—allowing radiologists to spend more time with patients.
“This new form of care model provides radiologists with more flexibility, potentially easing the sector’s high burnout rate. With new technologies and diagnostic capabilities, radiologists will need to learn new skills and capabilities to adapt,” authors concluded.
Matt joined Chicago’s TriMed team in 2018 covering all areas of health imaging after two years reporting on the hospital field. He holds a bachelor’s in English from UIC, and enjoys a good cup of coffee and an interesting documentary.