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“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.

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”.

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”.

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.

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.

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.

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.

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.

Julius Chapiro, Research Scientist, Department of Radiology and Biomedical Imaging, Yale University School of Medicine shares the benefits of multi-modality and 3D imaging for patients.