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RSNA 2016 in review: AI, machine learning and technology

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At RSNA 2016, the majority of significant new product announcements were modalities, not information technology. It almost seems that many radiology IT companies (or business segments) are planning to release new product introductions at HIMSS rather than at RSNA. While enterprise imaging remains the core radiology IT technology on display at RSNA, the big buzz this year was artificial intelligence and machine learning. As part of their Opening Session, Keith J. Dreyer, DO, PhD, and Robert M. Wachter, MD, discussed the good and the bad of the digital revolution in radiology. With artificial intelligence (AI) rapidly advancing thanks to events such as the ImageNet Large Scale Visual Recognition Challenge Competition, Dr. Dreyer believes AI will complement radiology and enable radiologists to become leaders in precision medicine; rather than becoming wary of AI, he said, radiology could work with AI to optimize the delivery of patient care.


Artificial Intelligence all set to Virtualize Radiology Brains

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Accelerating with an exponential growth, artificial intelligence (AI) is all set to move from experimental stages to live industry implementations and all is set to mark its presence across all industry verticals. AI is all about virtualizing human cognitive functions in the form of software brains. For organizations, harnessing AI is not optional, albeit it is critical to stay competitive. Gartner in its recent study (2018), predicts the business value derived from AI to reach $3.9 trillion by 2022. With the disruptive potential, the investments in AI are ever-increasing.


Algorithms begin to show practical use in diagnostic imaging

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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.


Deep Learning in Medical Imaging to Create $300 Million Market by 2021

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Deep learning, also known as artificial intelligence, will increasingly be used in the interpretation of medical images to address many long-standing industry challenges. This will lead to a $300 million market by 2021, according to a new report by Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare information technology industry. In most countries, there are not enough radiologists to meet the ever-increasing demand for medical imaging. Consequently, many radiologists are working at full capacity. The situation will likely get worse, as imaging volumes are increasing at a faster rate than new radiologists entering the field.


Imaging Technology News

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Burnout has become a popular buzzword in today's business world, meant to describe prolonged periods of stress in the workplace leading to feelings of depression and dissatisfaction with one's occupation. The topic has become so pervasive that the World Health Organization (WHO) addressed it at its 2019 World Health Assembly in Geneva in May, adding burnout to the 11th revision of the International Classification of Diseases (ICD-11) -- although classifying it as an "occupational phenomenon" rather than a medical condition. Healthcare itself is not immune to burnout, and a recent study in Journal of the American College of Radiology demonstrates it is taking a toll on pediatric radiologists in particular. The study surveyed Society of Pediatric Radiology (SPR) members and found nearly two-thirds expressed at least one symptom of burnout. While burnout is a complicated phenomenon and no two people experience it the same way, a commentary on the study suggests artificial intelligence (AI) could help alleviate some of the difficulties that can lead to burnout.