Collaborating Authors


Aidoc and Gleamer Partner To Expand the Use of AI in Medical Imaging


This partnership will help health systems address the increasing volume of medical images and the worldwide radiologist labor shortage. Integration of Boneview into Aidoc's AI platform will give many more clinicians access to a tool to help them identify fractures in limbs, pelvis, thoracic and lumbar spine, and rib cage. Aidoc's end-to-end AI platform already includes numerous third-party AI vendors including Imbio, Riverain, Subtle, Icometrix and ScreenPoint. Over 152 million X-rays are performed every year in the US. Although there are about 37,000 radiologists in the US, they are not evenly distributed.

Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Roadmap on Signal Processing for Next Generation Measurement Systems Artificial Intelligence

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning Artificial Intelligence

In an emergency room (ER) setting, the diagnosis of stroke is a common challenge. Due to excessive execution time and cost, an MRI scan is usually not available in the ER. Clinical tests are commonly referred to in stroke screening, but neurologists may not be immediately available. We propose a novel multimodal deep learning framework, DeepStroke, to achieve computer-aided stroke presence assessment by recognizing the patterns of facial motion incoordination and speech inability for patients with suspicion of stroke in an acute setting. Our proposed DeepStroke takes video data for local facial paralysis detection and audio data for global speech disorder analysis. It further leverages a multi-modal lateral fusion to combine the low- and high-level features and provides mutual regularization for joint training. A novel adversarial training loss is also introduced to obtain identity-independent and stroke-discriminative features. Experiments on our video-audio dataset with actual ER patients show that the proposed approach outperforms state-of-the-art models and achieves better performance than ER doctors, attaining a 6.60% higher sensitivity and maintaining 4.62% higher accuracy when specificity is aligned. Meanwhile, each assessment can be completed in less than 6 minutes, demonstrating the framework's great potential for clinical implementation.

On the Opportunities and Risks of Foundation Models Artificial Intelligence

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches Artificial Intelligence

With the development of computer-aided diagnosis (CAD) and image scanning technology, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital pathology. Since 2004, WSI has been used more and more in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computers, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists obtain more stable and quantitative analysis results, save labor costs and improve diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning in WSI segmentation, classification, and detection are reviewed continuously. Finally, the existing methods are studied, the applicabilities of the analysis methods are analyzed, and the application prospects of the analysis methods in this field are forecasted.

New medical imaging partnership to simplify AI access, workflow


TeraRecon, Ambra Health and EnvoyAI announced a new collaboration March 6 to create an artificial intelligence (AI)-enhanced image exchange workflow for healthcare providers, according to a recent press release. "Through our partnerships with Ambra Health and TeraRecon, our technology can reach remote, single physician practices while also meeting the most demanding clinical needs of the largest integrated health systems," said Jake Taylor, CTO of EnvoyAI. The new partnership will allow providers to implement AI through an Ambra image exchange workflow while having full access to an AI algorithm portfolio available on the EnvoyAI Exchange, ultimately leveraging healthcare provdiers' current PACS and the full range of TeraRecon viewers, according to the press release. "This interoperability partnership gives healthcare providers in the Ambra network access to bleeding edge AI technology for advanced image visualization, providing the very latest technological innovations to continually improve patient outcomes and care," said Andrew Duckworth, vice president of business development for Ambra Health, in a prepared statement. The new AI workflow will be on display until Thursday, March 8. at HIMSS18 annual meeting in Las Vegas.

Artificial Intelligence Helps Improve MRI Imaging of Strokes


High resolution MRI scans of the brain can take around thirty minutes to perform, but in the case of a stroke this can be much too long to wait. Typically, if MRI is used, a stroke patient is rushed through so that fewer imaging slices are taken, resulting in a much lower quality image. Compared to high end scientific studies that produce imaging slices around a millimeter apart, a quick scan can have the slices spaced up to seven millimeters from each other. At this resolution, many of the automated computer vision algorithms that help to understand the images fail to work, and precise diagnosis is a serious challenge. Researchers at MIT working with clinicians at Massachusetts General Hospital have been working on using artificial intelligence techniques to be able to use high resolution scans of different patients taken previously to significantly improve the image quality of MRI scans of incoming stroke victims.

MedyMatch, Capital Health to develop artificial intelligence for the emergency room


Stealthy MedyMatch emerged in February with plans to improve emergency room care using cognitive analysis and artificial intelligence. Now, in its first collaboration with a U.S. hospital, the company is developing its first real-time decision-support tool using data from New Jersey-based Capital Health. Under the agreement, Capital Health will supply Israel-based MedyMatch with anonymized data to help it develop the tool, which will target stroke patients. It will analyze medical images and provide the ER radiologist with information to help him or her determine the course of treatment. It combines "deep vision, advanced cognitive analytics and artificial intelligence" to analyze images and identify anomalies that may be invisible to the human eye.