Years of technological innovation has meant that off-the-shelf computing equipment has become so powerful that AI Deep Learning (the process of generalising 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 organisation 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.

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

Artificial intelligence seems to be everywhere these days and is showing tremendous potential to revolutionise 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 analyse 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.

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.

Fuminari Tatsugami, MD, PhD

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.