// EDITORIAL

Years of technological innovation has meant that off-the-shelf computing equipment has become so powerful that AI Deep Learning (the process of generalizing patterns from large numbers of datasets) is possible at a reasonable cost. The availability of good quality, well-curated datasets has also improved, and new methods of AI learning have been invented that are better at mimicking humans.
Combining these advances has enabled the application of AI to the entire Radiology workflow, and this change has the potential to be transformational. We can all learn a lot from the use of AI in the research environment.
However, we must be cautious.
A great deal is now understood about the pitfalls of training AI with poor-quality or biased datasets and the merits of using a truly representative population for verification. In truth, the term ‘Artificial Intelligence’ as we use it in Radiology and Healthcare today is a misnomer. The AI algorithms being developed don’t understand the data that they use or the results that they produce.
Algorithms alone have no understanding of pathology, disease, patients, or even care, but they are definitely useful given the challenges facing healthcare providers. Particularly during a time when we are emerging from a global pandemic that has changed medicine forever.
Healthcare professionals are becoming increasingly ‘AI savvy’ and are asking the right questions to industry and partners. AI algorithms that can support humans but not replace them is an achievable and desirable goal for all parties. Increasingly, AI researchers are being challenged to demonstrate that their innovation works within a real workflow and not just in the test environment in the laboratory.
The appropriate use of AI to streamline the entire radiology workflow, from patient positioning on the scanner through to the final diagnostic process, can free-up professionals to spend more time with patients and create more time for non-routine work that demands their experience and skill. Embedding AI into the daily routine to create data driven workflows, in which the relevant information is provided to the clinician at the right stage of the process to enable optimal decision-making is probably the final challenge that requires vendors to work together.
No individual organization or company can currently deliver these next generation ‘smart workflows’ alone, but the goal of delivering improved healthcare for all is so compelling that alliances and ecosystems are forming to tackle this ultimate challenge at such a critical time.
Canon is contrinbuting through Altivity - our bold new approach to AI innovation.
Through this, we are strengthening our focus to help clinicians leverage data and transform it into essential insights that improve patient outcomes and streamline workflows across the board.

We can harness AI now for the benefit of us all. It is already creating a positive difference in image quality, reconstruction speed, enabling reduced radiation dose and accelerating workflows. However, the human expertise of clinicians remains essential and always will.

Webinar Report 1

Maximizing the Potential of Artificial Intelligence (AI) - Based Diagnostics

Dr. Ken Sutherland , Jamie Keena, Prof. Sotirios A. Tsaftaris

From its offices in Edinburgh, Scotland, UK, Canon Medical Research Europe, develops transformative Deep Learning technologies that have potential to improve medical diagnostics. Canon’s team of computer scientists, software engineers and clinical researchers work together with some of the world’s top healthcare experts on this emerging field. VISIONS talked to Dr. Ken Sutherland, President of Canon Medical Research Europe and Professor Sotirios ‘Sotos’ Tsaftaris, Chair in Machine Learning and Computer Vision at The University of Edinburgh, and the Canon Medical/ Royal Academy of Engineering Research Chair in Healthcare AI, about the prospects for AI in meeting new challenges in healthcare.

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Webinar Report 2

Exploring Intelligent Healthcare – AI Today and Beyond

Professor Eliot L, Siegel, MD, Professor Bram van Ginneken, PhD

Artificial intelligence seems to be everywhere these days and is showing tremendous potential to revolutionize much of our work. Through Altivity, its unique approach to AI, Canon Medical is leveraging AI to create richer, more precise images, to aggregate and analyze biological and imaging data, to improve critical clinical insights, and to streamline workflows, so that precision medicine becomes a reality in our value-based care world.

Rich Mather, President of Canon Medical Research USA, spoke to two long time champions of digital imaging and Artificial Intelligence – Professor Eliot L, Siegel, Vice Chair in the Department of Radiology and Nuclear Medicine at the University of Maryland School of Medicine, US, and Professor Bram van Ginneken, a Professor of medical image analysis at Radboud University Medical Center, the Netherlands. Together, they reflect on the early days of AI and compare it with today’s promising breakthroughs and the bright future of intelligent healthcare.
Moderator : Rich Mather, PhD President of Canon Medical Research USA, INC. Vernon Hills, IL USA

Breaking New Ground with AI

“I think we're just beginning to realize the groundbreaking potential of AI. It will undoubtedly have a major positive impact on accuracy, discoverability, safety and efficacy in diagnostic imaging. And it will revolutionize the practice of diagnostic radiology. It will allow radiology and radiologists to stay relevant and I think finally enter the dawn of the personalized precision medicine era”

Professor Eliot L, Siegel

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AI in Radiology: From Promise to Invisibility?

“It's doing AI and Deep Learning, but the typical user doesn't see that because it's built into the product,” said Professor van Ginneken. “AI may eventually become invisible, but it will still be super useful.”

Professor van Ginneken

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Webinar Report 3

The Reality of AI in Radiology Today

Professor Eliot L. Siegel, MD, Professor Peter Chang, MD, Dr. Cindy Siegel, Professor Patrik Rogalla, MD, Tom Szostak

Canon Medical works together in close collaboration with leading healthcare professionals to gather their experiences and their feedback as to what may help them and their patients even further when using AI. Our partnership with customers fuels our continual innovation. We invited four of the world’s leading radiology specialists to share their views on the AI landscape in clinical practice today.

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White paper

Clinical Usefulness of 2D Thin-Slice Images Made Possible by 3T DLR-MRI-
- Maximizing Utility in the Field of Orthopedics -

Takahide Kakigi, MD, PhD

Vantage Galan 3T / ZGO*, a high-end MRI system manufactured by Canon Medical Systems Corporation, has been in operation at Kyoto University Hospital, Japan, since 2019. The system was updated to the latest system in 2020. This lecture focuses on the clinical usefulness of high-resolution 2D thin-slice images and MPR images obtained using a 3T Deep Learning Reconstruction (DLR) MRI system, in the field of orthopedics.

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* Vantage Galan 3T / ZGO is not commercially available in all country

White paper

Evaluation of Coronary CT with Super-Resolution Precise IQ Engine (PIQE)

Fuminari Tatsugami

This lecture presents a brief report on the current status of Deep Learning Reconstruction (DLR) followed by a discussion of the author's actual clinical experience with Canon’s Super-Resolution DLR known as Precise IQ Engine (PIQE), which is a next-generation DLR method.

Read the full white paper Find out more PIQE

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