AI in Digital Imaging
Artificial Intelligence (AI) models are becoming more accessible and widespread in medical practices. It can be used to improve efficiencies, streamline processes, and improve biometric analysis. It is also proving to be beneficial in neoplasm detection and characterization. This can reduce the need for unnecessary interventional procedures and improve patient health planning and delivery.
AI is used in CT, MRI, Ultrasound, x-ray, nuclear medicine, and bone density scanning. I have included some wonderful studies and peer-reviewed articles on the subject of AI in the radiological space. I have included a link to my blog on AI in Ultrasound as it is already shaping how we perform and report ultrasounds
Integration of AI can be an intimidating prospect to radiographers, as it changes the landscape of the profession but overwhelmingly the reviews are positive among early adopters with some valid concerns which need addressing for clinical use
Botwe, B.O., Antwi, W.K., Arkosh, A., et al. (2021). Radiographers’ Perspective on the Emerging Integration on Artificial Intelligence into Diagnostic Imaging: The Ghana Study. Journal of Medical Radiation Sciences, 68(3): 260-268.https://doi.org/10.1002/jmrs.460
The future of medicine is AI, with massive global investment in the technology. It can be used in infinite applications, including drug development, decreased workload, disease management and prevention, and the synthesis of large data files for functional application
Habuza, T., Navaz, A.N., Hashim, F. et al., (2021). AI Applications in Robotics, Diagnostic Image Analysis, and Precision Medicine: Current Limitations, Future Trends, Guidelines on CAD Systems for Medicine. Informatics in Medicine Unlocked, 24.https://doi.org/10.1016/j.imu.2021.100596
Radiographers must be adaptable to new technology as the field is one of constant evolution and expansion. AI has emerged as a new trend which will drastically change the professional landscape of radiology, requiring adaptability and learning from radiographers and employers
Hardy, M. & Harvey, H. (2019). Artificial Intelligence in Diagnostic Imaging: Impact on the Radiography Profession. British Institute of Radiology, 93(1108).https://doi.org/10.1259/bjr.20190840
Looking at the impact of COVID-19 on the remote and telehealth landscape, Deloitte divulges its predictions on the future of AI
Kudumala, A., Ressler, D., & Miranda, W. (2020). Scaling up AOI ACross the Life Sciences Value Chain. Deloitte Insight. Retrieved fromhttps://www2.deloitte.com/us/en/insights/industry/life-sciences/ai-and-pharma.html
This peer-reviewed article evaluates the research available on deep learning in the practice of ultrasound diagnosis and evaluation processes to improve tumor detection, staging, and biometric analysis as well as a wealth of other practical applications in medicine.
Liu, S., Wang, Y., Lei B. et al., (2019). Deep Learning in Medical Ultrasound Analysis: A Review. Engineering, 5(2): 261-275.https://doi.org/10.1016/j.eng.2018.11.020
AI can be taught to detect placental margins to better predict pregnancy outcomes
Looney, P., Stevenson, G.N., Nicolaides, K.H. et al. (2018). Fully Automated, Real-time 3D Ultrasound Segmentation to Estimate First Trimester Placental Volume Using Deep Learning. JCI Insight, 3(11).https://doi.org/10.1172/jci.insight.120178
This large peer reviewed analysis delves deeply into the applications, limitations, and production of multiple AI technologies to improve provider care
Lundstrom, C. & Lindvall, M. (2023). Mapping the Landscape of Care Providers’ Quality Assurance Approaches for AI in Diagnostic Imaging. Journal of Digital Imaging, 36: 379-387.https://doi.org/10.1007/s10278-022-00731-7
This landmark study surveilled physicians of a wide demographic representation to determine the public opinion on AI in the medical field
Swar, S., Dent, A., Faust, K. et al. (2019). Physician Perspectives on Integration of Artificial Intelligence into Diagnostic Pathology. NPJ Digital Media,https://doi.org/10.1038%2Fs41746-019-0106-0