Artificial intelligence (AI) sizzled at the Radiological Society of North America's (RSNA) 2018 meeting as vendors promoted new, old and unconventional technologies as means to increase efficiency throughout imaging. New releases were unveiled in imaging modalities, notably magnetic resonance imaging (MRI) and computed tomography (CT). But excitement was muted compared to enthusiasm for AI, which has been high for two years. Interest in AI was stoked by multiple medical societies at their annual meetings in the weeks leading up to RSNA 2018. The Radiological Society of North America contributed further through its decision to set up a machine learning showcase in the back of the North exhibit hall. Enthusiasm for enterprise imaging (EI) increased with the vendor promotion of AI software that promised to streamline workflow. This "coattail effect" was seen in multimodality exhibits including those of GE, Siemens, Philips and Canon, which promoted the use of AI not just to improve scanners, but to boost the performance of picture archive and communication systems (PACS) and vendor neutral archives (VNA) as well.

Ed Ikeguchi, chief medical officer at AiCure, an artificial intelligence (AI) and data analytics company, said 2018 was a year filled with change – both positive and negative. On the upside, the pharmaceutical industry more broadly adopted innovative technology leveraging AI and machine learning for use across R&D as well as commercial development, he told us. Regulators also got on board, with the US Food and Drug Administration (FDA) releasing a statement backing the idea of AI-enabled technology and encouraging its use in health care. Larger technology companies like Google, Apple, and Amazon also have shown greater investment and interest in the health care space, Ikeguchi noted. As one example, Amazon, JP Morgan, and Berkshire Hathaway teamed up to form a new company that aims to address US employee health care.

The growth of artificial intelligence in the healthcare industry has been astounding. Each day, we see new articles about AI-related advances in medical imaging, diagnosis, and health informatics. Health service providers are finding more and more ways to match patients with treatment options, improve the detection of disease, and analyze patients' data to give them more insight into their individual health profiles. AI is also used to improve clinical payment structures, develop treatment plans, and optimize coordination between clinical staff. The U.S. patent system--a key player in protecting R&D innovation--is also experiencing tremendous growth, with patent filings for AI-related technologies trending upward.

AI, the impact of which is simultaneously over-hyped and under-rated in most sectors, is catalyzing revolutionary use cases in healthcare. Deep learning has come a long way from identifying cats and dogs and can now perform independent image-based diagnosis with comparable or better accuracy than (human) doctors. X-Ray or scan-based diagnosis of tumours, fractures, strokes, electrodiagnosis can be entirely done by algorithms. While several of these options have received FDA approval and starting a clinical trial or beginning to be used in production, researchers are attempting selfie diagnosis to detect 50 diseases from abnormalities in eye colour (Nature,2018) and diagnosis from (molecules emitted from) body odour. Using data from wearables or behavioural observations such as changes in gait, driving patterns, mouse usage etc., machine learning algorithms can predict the onset of physiological (particularly neurological or cardiovascular) or psychiatric disorders, assist management of chronic conditions such as diabetes or epilepsy, and even raise real-time warnings.

Posted on January 15th, 2019 by Dr. Francis Collins Thanks to advances in wearable health technologies, it's now possible for people to monitor their heart rhythms at home for days, weeks, or even months via wireless electrocardiogram (EKG) patches. In fact, my Apple Watch makes it possible to record a real-time EKG whenever I want. For true medical benefit, however, the challenge lies in analyzing the vast amounts of data--often hundreds of hours worth per person--to distinguish reliably between harmless rhythm irregularities and potentially life-threatening problems. Now, NIH-funded researchers have found that artificial intelligence (AI) can help. A powerful computer "studied" more than 90,000 EKG recordings, from which it "learned" to recognize patterns, form rules, and apply them accurately to future EKG readings.

Artificial intelligence (AI) was by far the hottest trend discussed in sessions and across the expo floor at the world's largest radiology conference, the 2018 Radiological Society Of North America (RSNA). At the meeting in late November, there was an explosion of AI and deep learning algorithms across the expo floor. How machine learning will impact medical imaging was the key takeaway from the opening session, where examples of how AI will alter medical imaging in the near future were highlighted. Here is an overview of the types of AI software being developed and a few examples from RSNA that are specific to cardiovascular imaging. Artificial intelligence has been a growing topic in past years at RSNA, but this year several companies showed products that recently gained U.S. Food and Drug Administration (FDA) market clearance.

As Healthcare Analytics News reported earlier this month, a recent study published in the Journal of the National Cancer Institute revealed that artificial intelligence (AI) outperformed humans in identifying cervical precancer lesions in a primate model. Now, another study published last week demonstrated that AI was able to effectively screen a human population for pre-cancerous lesions as well. These results are definitely promising and have inspired many to push AI forward, much faster. How can we as physicians and healthcare executives leverage this technology to improve patient outcomes? And with the U.S. Food and Drug Administration (FDA) now defining ways to get AI in medicine approved as well, how are our hospitals going to keep up with these new technologies as they are rolled out?

Computer vision has made the transition from being the subject of sci-fi movies to an actual technology which you can find in top hospitals. This visual branch of artificial intelligence is helping more doctors better diagnose patients, prescribe the right treatments and monitor the evolution various diseases. Technopedia defines computer vision as a distinctive field of computer science which helps computers to see, identify, and process images in a way which is similar to the way humans perform this task. It is part of artificial intelligence since to identify objects and take decisions based on what it sees it is necessary to make an in-depth analysis. Predicting the applications of a computer vision solution for medical use has to do with extending current ways this technology is already being used and adding a layer of creativity and imagination.

Oregon's two senators are urging the head of the U.S. Food and Drug Administration to update federal regulations to permit interstate commerce of food products containing a key non-psychoactive ingredient of cannabis.

The image above has been cropped. Could cervical cancer be brought under control? Not quite, but the results of a study published in the Journal of the National Cancer Institute seem promising. Researchers from the National Institutes of Health and Global Good have developed a deep learning algorithm that can analyze digital images of a woman's cervix and identify precancerous changes that require medical attention -- with more accuracy than human experts. The team used comprehensive datasets to train the algorithm to recognize patterns in complex visual inputs, like medical images.