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RSNA 2016 in review: AI, machine learning and technology


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.

Medical imaging, AI, and the cloud: what's next? - Microsoft Industry Blogs


Today marks the start of RSNA 2020, the annual meeting of the Radiological Society of North America. I participated in my first RSNA 35 years ago and I am super excited--as I am every year--to reconnect with my radiology colleagues and friends and learn about the latest medical and scientific advances in our field. Of course, RSNA will be very different this year. Instead of traveling to Chicago to attend sessions and presentations, and wander the exhibits, I'll experience it all online. While I will miss the fun, excitement, and opportunities to connect that come with being there in person, I am amazed by what a rich and comprehensive conference the organizers of RSNA 2020 have put together using the advanced digital tools that we have at hand now.

Imaging Technology News


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.

A Multisite, Report-Based, Centralized Infrastructure for Feedback and Monitoring of Radiology AI/ML Development and Clinical Deployment Machine Learning

An infrastructure for multisite, geographically-distributed creation and collection of diverse, high-quality, curated and labeled radiology image data is crucial for the successful automated development, deployment, monitoring and continuous improvement of Artificial Intelligence (AI)/Machine Learning (ML) solutions in the real world. An interactive radiology reporting approach that integrates image viewing, dictation, natural language processing (NLP) and creation of hyperlinks between image findings and the report, provides localized labels during routine interpretation. These images and labels can be captured and centralized in a cloud-based system. This method provides a practical and efficient mechanism with which to monitor algorithm performance. It also supplies feedback for iterative development and quality improvement of new and existing algorithmic models. Both feedback and monitoring are achieved without burdening the radiologist. The method addresses proposed regulatory requirements for post-marketing surveillance and external data. Comprehensive multi-site data collection assists in reducing bias. Resource requirements are greatly reduced compared to dedicated retrospective expert labeling.



This is a curated list of medical data for machine learning. This list is provided for informational purposes only, please make sure you respect any and all usage restrictions for any of the data listed here. The National Library of Medicine presents MedPix Database of 53,000 medical images from 13,000 patients with annotations. These 1112 datasets are composed of structural and resting state functional MRI data along with an extensive array of phenotypic information. Also has clinical, genomic, and biomaker data.