The center is only about a year old, but it has already built important capabilities. Its goal is not basic research, but improving clinical practice within the two hospitals and the healthcare system in general. According to the CCDS Executive Director, Dr. Mark Michalski, in order for this technology to actually affect care there are several key prerequisites:
The hype around artificial intelligence (AI) in medical imaging has led to plenty of discussions of its impact in clinical and academic spaces. To explore current and future implementations of AI in medical imaging at academic institutions, Health Imaging spoke with Curtis Langlotz, PhD, Stanford University's Medical Informatics Director for Radiology. Health Imaging: Where do you think AI will first be deployed in medical imaging? Curtis Langlotz, PhD: Over the next decade, AI will be deployed throughout the image life cycle from image production to image interpretation. For example, machine learning algorithms will produce clearer images using less radiation and will alert technologists to suboptimal images at the scanner console.
S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE).
Algorithms based on machine learning and deep learning, intended for use in diagnostic imaging, are moving into the commercial pipeline. However, providers will have to overcome multiple challenges to incorporate these tools into daily clinical workflows in radiology. There now are numerous algorithms in various stages of development and in the FDA approval process, and experts believe that there could eventually be hundreds or even thousands of AI-based apps to improve the quality and efficiency of radiology. The emerging applications based on machine learning and deep learning primarily involve algorithms to automate such processes in radiology as detecting abnormal structures in images, such as cancerous lesions and nodules. The technology can be used on a variety of modalities, such as CT scans and X-rays.
As industry experts continue to explore artificial intelligence (AI) applications in radiology, the question remains of whether AI applications can and will add value, including in new knowledge and information to provide patients with better outcomes at lower costs. In a new editorial published in JACR by a team of researchers from the department of radiology at Massachusetts General Hospital and Harvard Medical School in Boston, the "big data" consuming technologies of AI and machine learning are evaluated in terms of opportunities, challenges, pitfalls and criteria for success. "For radiologists, adding value includes establishment of more efficient work processes and improved job satisfaction," said lead author of the study James H. Thrall, MD, chairman emeritus of the department of radiology at Massachusetts General Hospital. "The goal of this perspective is to help create a framework, apart from a discussion of AI technology per se, for developing strategies to explore the potential of AI in radiology and to identify a number of scientific, cultural, educational and ethical issues that need to be addressed." The researchers note that although the ultimate role of AI in medicine is not yet clear, AI will provide advanced tools to more thoroughly analyze imaging data.