Recently through my with work with Ellumen Inc., I have been been contributing to a new blog series on AI (Artificial Intelligence) innovation in medical imaging. I am one of the experts along with Dr. Iyanuoluwa Odebode to be contributing to the Ellumen blog series. Dr. Iyanuoluwa Odebode has a master’s in bioinformatics from Morgan State University and Ph.D. in information systems (machine learning/AI) from the University of Maryland Baltimore County.
So far three articles I have contributed to have appeared on the Ellumen website. In case you missed it below is a brief summary of these articles. Be sure to check them out for additional details.
Could AI Be the Radiologist’s Best Friend?
The Could AI Be the Radiologist’s Best Friend? article published on February 17, 2021. The article discusses how AI has the potential to alleviate the demand on radiologists by doing preliminary evaluations on medical images and organizing imaging workflows to improve efficiency. The article mentions the five most common use cases of AI in radiology today: 1) optimizing workflow for productivity, 2) tagging images so critical patients are the first reviewed, 3) automating part of the image analysis, 4) enhancing imaging quality, and 5) providing decision support, and presents an excellent graphic to accompany this to improve understanding. The article further discusses how by positioning AI technology as a useful and supplemental tool, radiologists and clinicians can reap the benefits while their confidence in using AI grows and skepticism fades.
AI for Medical Imaging Research: A Guide to Accessible Tools and Resources
The AI for Medical Imaging Research: A Guide to Accessible Tools and Resources article published on March 22, 2021. The article discusses numerous tools and resources that currently exist that researchers can use to help develop AI algorithms for medical imaging. The article also provides an extensive list of medical imaging datasets with high quality images and annotations that already exist. Further, it is discussed how it is hoped with improved awareness of the numerous tools and data sources available to AI researchers today, participation in medical imaging research and progress to accelerate AI in medical imaging transformation will be made.
AI Tools in Triage Lead to Faster Diagnoses
The AI Tools in Triage Lead to Faster Diagnoses article published on May 4, 2021. This article discusses utilizing AI as a triage mechanism and in support of more efficient workflows for medical imaging diagnosis. It is known that patient outcomes are directly correlated with speed and in many cases patient care is extremely time sensitive. The article discusses how AI can be be utilized to reduce the time required for an MRI scan from one hour to 15 minutes and by doing so reduce the noise in images and allow more patients to be scanned by the same MRI machine each day. The article also presents details on how a deep learning neural network can be trained using labeled images of diseases and normal conditions present and shows a graphic to further understanding. The neural network can be leveraged to provide radiologists with a screening tool before they look at a series of images, allowing them to more quickly move through the series and form an opinion on a diagnosis.
Future Articles for Ellumen’s Blog Series on AI Innovation in Medical Imaging
It is believed that AI in medical imaging can lead to better outcomes for patients. Radiologists who recognize the importance of AI’s medical imaging transformation will lead to improvements in patient care and accuracy of a diagnosis. Be sure to keep an eye out for new forthcoming articles on the Ellumen website related to AI innovation in medical imaging and feel free to reach out to experts at Ellumen to help explore the potential of AI to solve medical imaging research needs.
Permalink