AI and ML technologies hold great potential for use in medical imaging to support more precise clinical decision-making and improved patient experience. Such tools can help practitioners identify pathologies more quickly, develop more personalized treatment plans, reduce delays in diagnosis, and even limit the need for patients to repeat scans.
Advances in AI-driven 3-D visualization, augmented reality, and inter-operative capabilities in medical imaging promise to facilitate more exact identification of abnormalities and inform targeted interventions. These technologies enable practitioners to target areas of concern with greater precision and speed. In some cases, this capability can conceivably minimize the need to perform invasive procedures for diagnosis. In addition, by reducing imaging noise, AI-enabled devices can help medical imaging professionals optimize dosing in scans to minimize patient radiation exposure.
AI- and ML-driven tools also offer exciting prospects for improving patient experience. For example, according to a 2020 Health IT Analytics article, the Mass General Brigham Center for Clinical Data Science developed an ML-driven tool to detect motion during a scan and alert technicians to the possibility a re-scan is required while the patient is still present, reducing the likelihood that the patient will have to make a return trip for additional imaging.
One of the innovative approaches to medical imaging that AI enables is radiomics, described as advanced imaging analytics. This technology uses AI and ML to extract and analyze a wealth of quantitative data points from scanned images, harvesting insights that might otherwise be discovered only through invasive procedures. By detecting and analyzing features not visible through conventional imaging, radiomics can develop a profile of a tumor’s biomarkers to inform more personalized care, track disease progression, and monitor responses to treatment.
Given the breadth of opportunities these technologies offer for advances in the field of medical imaging, it is probable they will continue to proliferate and evolve. Still, issues surrounding data access, model training, and navigating ethical concerns are among the challenges that remain on the road toward more widespread usage of many AI and ML applications in medical imaging. As stated in the Health IT Analytics article:
Even with all these advancements, however, the industry still struggles with several foundational problems. Limited data access, a lack of provider education and training, and poor technology integration are all obstacles that many organizations have yet to overcome.
Equipping healthcare workforces with the knowledge and skills to work effectively with AI and ML tools will be critical to deploying these technologies on a larger scale. With the help of data science training, practitioners will be prepared to embrace these tools for improved patient care and outcomes.
(Updated for 2022) With a long-term strategy—and with investment in the training and infrastructure necessary to implement it—the healthcare industry can experience a data revolution. Given today’s abundance of potential data sources, the widespread adoption of electronic health records (EHRs), and advances in AI, ML, NLP, and other data science technologies, the healthcare industry is poised for a groundbreaking overhaul.
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