Standardised and well-structured data, as well as the definition of clear objectives, are indispensable prerequisites for artificial intelligence implementation into clinical processes. ‘Ask not what artificial intelligence can do for you but what you can do for artificial intelligence!’ This variation on John F Kennedy’s famous quote comes from Dr Ben Glocker, Senior Lecturer in Medical…
Machine learning is increasingly helping radiologists to acquire faster and better quality images, and measure heart function. This is just the tip of the iceberg; artificial intelligence has far more to bring to the heart, explained Daniel Rueckert, Head of the Department of Computing at Imperial College London, during CMR 2018. Machine learning (ML) is becoming a valuable assistant for…
Neuroscientist Lynda Chin MD, Founder and CEO of Real-world Education Detection and Intervention, has little doubt: ‘Artificial intelligence to the rescue,’ she proclaimed in her keynote address at the Artificial Intelligence and Machine Learning Summit, held in Las Vegas this spring. ‘We need a system and analytics to interpret data!’ she urged, despite being well aware that building a…
Using a new approach called 'reinforced learning', researchers have taught an artificial intelligence (AI) to responsibly choose the right amount of chemo- and radiotherapy for glioblastoma patients. The technique, which is insprired by behavioural psychology, has given the AI the ability to master the tightrope walk between effective tumor shrinkage and the medications' severe side effects.
Machine learning is adding a new dimension to pathology and already outperforming experts during some tasks, according to several speakers at the 14th European Congress on Digital Pathology (ECDP) who revealed up-to-date developments. However, whilst AI is set to herald a new future for digital pathology, Johan Lundin, associated professor for biomedical informatics and research director at the…
Researchers have shown for the first time that a form of artificial intelligence or machine learning known as a deep learning convolutional neural network (CNN) is better than experienced dermatologists at detecting skin cancer. In a study published in the leading cancer journal Annals of Oncology, researchers in Germany, the USA and France trained a CNN to identify skin cancer by showing it more…
Tracy Accardi, Hologic’s Vice President (Global R&D), spoke of the importance of innovation, tomosynthesis, artificial intelligence/deep learning and open dialogue with the radiology community. Hologic addresses a broad spectrum of gynaecological, perinatal, aesthetic, skeletal and breast women’s health issues. To enhance this approach, Accardi, explained the importance of working closely…
Some time in the distant future artificial intelligence (AI) systems may displace radiologists and many other medical specialists. However, in a far more realistic future AI tools will assist radiologists by performing very complex functions with medical imaging data that are impossible or unfeasible today, according to a presentation at the RSNA/AAPM Symposium during the Radiological Society of…
Google AI has made a breakthrough: successfully predicting cardiovascular problems such as heart attacks and strokes simply from images of the retina, with no blood draws or other tests necessary. This is a big step forward scientifically, Google AI officials said, because it is not imitating an existing diagnostic but rather using machine learning to uncover a surprising new way to predict these…
Researchers used machine learning techniques, including natural language processing algorithms, to identify clinical concepts in radiologist reports for CT scans, according to a study conducted at the Icahn School of Medicine at Mount Sinai and published in the journal Radiology. The technology is an important first step in the development of artificial intelligence that could interpret scans and…
Machine learning is playing an increasing role in computer-aided diagnosis, and Big Data is beginning to penetrate oncological imaging. However, some time may pass before it truly impacts on clinical practice, according to leading UK-based German researcher Professor Julia Schnabel, who spoke during the last ESMRMB annual meeting. Machine learning techniques are starting to reach levels of human…
Johns Hopkins Kimmel Cancer Center researchers developed a single blood test that screens for eight common cancer types and helps identify the location of the cancer. The test, called CancerSEEK, is a unique noninvasive, multianalyte test that simultaneously evaluates levels of eight cancer proteins and the presence of cancer gene mutations from circulating DNA in the blood. The test is aimed at…
Luke Skywalker’s bionic hand is a step closer to reality for amputees in this galaxy. Researchers at the Georgia Institute of Technology have created an ultrasonic sensor that allows amputees to control each of their prosthetic fingers individually. It provides fine motor hand gestures that aren’t possible with current commercially available devices. The first amputee to use it, a musician…
Automation might be the solution for many of the challenges radiologists and clinicians face today. Toshiba Medical, a Canon Group, is currently pushing the boundaries of what automation can accomplish and presenting their project in progress at this year's RSNA. Overwhelming volume of clinical data every day, limited access to relevant clinical information, missed findings and lack of…
International medical imaging IT and cybersecurity company Sectra is launching vendor-neutral access to machine learning in its enterprise imaging platform. Sectra customers will be able to utilize Sectra’s own machine learning applications as well as virtually any application, regardless of vendor. This will give healthcare providers a unified entry point to a best-of-breed portfolio. It will…
Carestream Health is demonstrating advanced imaging analytics software tools designed to enhance value in the delivery of medical care as part of its Clinical Collaboration Platform at the SIIM Machine Learning Showcase being held Sept 26-27 at Johns Hopkins Hospital in Baltimore, Md.
In the nineties Deep Blue, the famous chess computer, defeated Kasparow. Only a year ago Google’s Deepmind managed to master the ancient Chinese Go, known for its utmost complexity.
Celebrating 40 years of PCI, cardiologists fret over their future with big data, machine learning and robots.
Language recognition on the smartphone, spam filters in the e-mail programme, personalised product recommendations by Amazon or Netflix – all share one feature: they are based on an algorithm that recognises patterns in a set of data. This artificial generation of knowledge is called machine learning.
With the help of artificial intelligence, computers are to simulate human thought processes. Machine learning is intended to support almost all medical specialties. But what is going on inside an AI algorithm, what are its decisions based on? Can you even entrust a medical diagnosis to a machine? Clarifying these questions remains a central aspect of AI research and development.
