By Elad Walach
As medical imaging technology such as MRI and CT becomes more precise and accessible, radiology is increasingly becoming a key foundation of modern medicine, helping to guide diagnostics, treatment, and prognoses. But the growing utility of medical imaging also has its downside: Since the volume of imaging is skyrocketing and the number of radiologists has plateaued, an unsustainable status quo has emerged, characterized by growing workloads for overburdened physicians and subsequent bottlenecks.
Despite these challenges, transformative new technologies, many powered by AI and machine learning, promise to redefine the practice of radiology in ways that will significantly improve efficiency, diagnostic quality, and medical treatment. Radiologists' roles will certainly evolve in the coming years in light of advancing technologies; this can be unsettling, but adaptation to these changes is essential to keep doctors from raising the white flag under an ever-increasing workload.
Given AI's strengths in analyzing visual images, radiology is one of the medical specialties best positioned for AI disruption, proof of which is emerging from many quarters. At Stanford University in California, for example, researchers have developed a deep learning algorithm for evaluating chest X-rays, claiming that it is able to diagnose pneumonia cases better than radiologists working alone; whether true or not, this demonstrates AI's growing presence in the industry. The University of California San Francisco's Center for Digital Health Innovation, meanwhile, has partnered with GE Healthcare to build a library of deep learning algorithms for the analysis of medical images and texts. Researchers train the algorithms to better detect potentially dangerous abnormalities, generating faster and more accurate insights to help guide clinicians' treatment decisions.
AI adoption will help ease the overwhelming workloads hampering the profession, enabling radiologists to better perform vital tasks. Many radiologists analyze up to 100 scans a day, with most working 10- to 12-hour shifts. Expecting radiologists to maintain their current quantity of work in an increasingly demanding climate may lead to more errors and degradation in treatment; that is exactly where AI can help.
Teleradiology is another growing field that is helping to streamline untenable workloads. Remote radiologic coverage, aided by the growing prevalence of advanced PACS and fast-speed telecom infrastructures, means more radiologic analysis is being performed online to balance workloads between hospitals. Of course, as the field becomes increasingly digitized, concerns regarding the security of radiology data underscore the need for robust solutions that are proven to prevent breaches and safeguard patient information, as well as comply with regulatory requirements.
Workflow orchestration technology also promises to boost efficiency and alleviate bottlenecks. By channeling cases to the right recipient in the right order, this technology optimizes the efficiency of the read, especially in teleradiology settings. With the profession's growing appetite for solutions that match demand with supply, companies such as Conserus, Primordial, and Clario provide solutions that facilitate better collaboration across facilities for effective workflow orchestration.
The Future Is Now
The forward march of technological development has engendered some anxiety among radiologists. But while tech adoption will inevitably change the nature of radiologists' work, technology's clinical value will be in augmenting and complementing—not displacing—the work of radiology professionals. Radiologists empowered by AI will only encounter a new, more efficient stage of radiology, helping to focus their time and attention on the most crucial elements of their job.
In addition, it's important to note that image analysis is but one facet of a radiologist's job; other tasks, including discrepancy reviews, diagnostic reasoning, and patient-facing work such as invasive radiology, will still be performed by humans. Those tasks will simply be supported and enhanced by advancing technology.
The good news is that radiology has proven itself to be one of the most adaptable medical fields, with a propensity for integrating new transformative technologies, including the transition from film to digital and the advent of new imaging modalities including CT and MR. Radiology was also on the leading edge of adopting automatic dictation using speech recognition, and radiologists were among the first physicians to perform minimally invasive, image-driven surgeries. Adaptation to AI-augmented radiology and other developing technologies only dovetails with the profession's track record of innovative adoption.
The future of radiology is here—and the prognosis for all stakeholders is excellent.