Latest News and Press Releases
Host Roger Tomihama, MD, speaks with interventional radiologist Julius Chapiro, MD, about the future of artificial intelligence in interventional radiology, his work with AI in liver cancer treatment, and more.
- October 14, 2021
In honor of Liver Cancer Awareness Month, Dr. Julius Chapiro shares his motivation for treating patients with liver cancer and researching new therapies for the disease.
- August 05, 2019
“We look to the clinicians to give us the appropriate clinical questions, then we try to develop algorithms that would address those issues.” Yale biomedical engineer Lawrence Staib, PhD, on using artificial intelligence to help with liver cancer treatment.
- May 17, 2019
Taking artificial intelligence out of the black box: An “interpretable” deep learning system for liver tumour diagnosisSource: European Society of Radiology
Convolutional neural networks (CNN) have demonstrated the potential to become effective and accurate decision support tools for radiologists. A major barrier to clinical translation, however, is that the majority of such algorithms currently function like a “black box”.
- March 11, 2019Source: interventionalnews.com
Clinicians are calling for increased collaboration between computer scientists, biomedical engineers and interventional radiologists as machine learning is posited to play a more prominent role in interventional radiology (IR) procedures, from informing the initial diagnosis, through to patient selection and intraprocedural guidance. In a recent primer published in The Journal of Vascular and Interventional Radiology (JVIR), Brian Letzen, Clinton Wang and Julius Chapiro, all of the Yale School of Medicine, New Haven, USA, outline the clinical applications of machine learning for IRs, and visualise a future where artificial intelligence (AI) enables the elevation of the discipline to become, in Chapiro’s words, “the epitome of personalised medicine”.
- January 10, 2019Source: ITN - Imaging Technology News
AI in Tumor Diagnostics, Treatment and Followup Play Mute Current Time 0:27 / Duration 8:01 Non-Fullscreen TRENDING NOW The Debate Over Gadolinium MRI Contrast ToxicityMost Popular Radiology Topics of 2017FDA Clears Siemens Healthineers' Multix Impact Digital X-ray System Julius Chapiro, M.D., research faculty member and an interventional radiology resident at Yale University, describes how artificial intelligence (AI) can be used to revolutionize cancer diagnosis and treatment at the 2018 Radiological Society of North America (RSNA) annual meeting.
- December 04, 2018Source: AuntMinnie
Yale University researchers have developed an artificial intelligence (AI) algorithm for classifying liver lesions on MRI scans that explains the reason for its decisions. The algorithm could address concerns about the "black box" nature of AI, according to research presented at last week's RSNA 2018 meeting.
- December 03, 2018Source: diagnosticimaging.com
It should come as no surprise that AI was featured heavily in this year’s Radiological Society of North America convention. From booths to sessions to one-on-one conversations around the hall, it seemed like almost everyone had something to say about AI.
- June 05, 2018Source: Interventional News
Aaron Abajian and Julius Chapiro write about results from an early experiment in applying artificial intelligence (AI) and machine learning as a decision support system in interventional oncology.
- November 27, 2017
Julius Chapiro, Research Scientist, Department of Radiology and Biomedical Imaging, Yale University School of Medicine shares how multi-modality and 3-D imaging technologies continue to exponentially define and benefit the future of cancer care.