Cleveland Clinic creates Center for Clinical Artificial Intelligence


Health system envisions center as a hub of collaboration between physicians, researchers, computer scientists and statisticians to find new ways to use AI technologies for improving patient outcomes. The new Cleveland Clinic Center for Clinical Artificial Intelligence, launched by Cleveland Clinic Enterprise Analytics, will leverage machine learning, deep learning and natural language processing to power diagnostics, disease prediction, as well as treatment planning. "We realize that there's a lot of opportunity in the space of artificial intelligence to advance patient care and outcomes," says Aziz Nazha, MD, who has been appointed director of the center and associate medical director for AI. "The mission is to harness the power of artificial intelligence to improve healthcare delivery and medical research by focusing on the clinical needs of the patients." By bringing together specialists from various departments, including genetics, IT, laboratory, pathology and radiology, the center will develop innovative clinical AI applications such as machine learning algorithms designed to reduce the risk of hospital readmission and to predict patient response to cancer treatments, according to Nazha.

Self-healing 3D-printed gel has a future in robots and medicine


Robots might be a little more appealing -- and more practical -- if they're not made of hard, cold metal or plastic, but of a softer material. Researcher at Brown University believe they've developed a new material that could be ideal for "soft robotics." It's already demonstrated that it can pick up small, delicate objects, and it could form customized microfluidic devices -- sometimes called "labs-on-a-chip" and used for things like spotting aggressive cancers and making life-saving drugs in the field. The 3D-printed hydrogel is a dual polymer that's capable of bending, twisting or sticking together when treated with certain chemicals. One polymer has covalent bonds, which provide strength and structural integrity.

Artificial intelligence and medicine: Is it overhyped? Medical Design and Outsourcing


Artificial intelligence raises exciting possibilities for healthcare, but are companies promising more than they can deliver? But artificial intelligence's potential also comes with an incredible level of hype. "AI has the most transformative potential of anything I've seen in my life, and I graduated medical school 40 years ago. It's the biggest thing I've ever seen by far," prominent cardiologist and author Dr. Eric Topol told Medical Design & Outsourcing. "But it's more in promise than it is in reality."

Aging Is a Communication Breakdown - Issue 70: Variables


Johann Wolfgang von Goethe, the 18th-century poet and philosopher, believed life was hardwired with archetypes, or models, which instructed its development. Yet he was fascinated with how life could, at the same time, be so malleable. One day, while meditating on a leaf, the poet had what you might call a proto-evolutionary thought: Plants were never created "and then locked into the given form" but have instead been given, he later wrote, a "felicitous mobility and plasticity that allows them to grow and adapt themselves to many different conditions in many different places." A rediscovery of principles of genetic inheritance in the early 20th century showed that organisms could not learn or acquire heritable traits by interacting with their environment, but they did not yet explain how life could undergo such shapeshifting tricks--the plasticity that fascinated Goethe. A polymathic and pioneering British biologist proposed such a mechanism for how organisms could adapt to their environment, upending the early field of evolutionary biology.

Using machine learning for medical solutions


Pharmaceutical companies spend a lot of time testing potential drugs, and they end up wasting much of that effort on candidates that don't pan out. Kyle Swanson wants to change that. A master's student in computer science and engineering, Swanson is working on a project that involves feeding a computer information about chemical compounds that have or have not worked as drugs in the past. From this input, the machine "learns" to predict which kinds of new compounds have the most promise as drug candidates, potentially saving money and time otherwise spent on testing. Several prominent companies have already adopted the software as their new model.

25 DeepTech News Briefs


The Stanford Institute for Human-Centered AI officially launched today. Stanford HAI seeks to become an interdisciplinary global AI hub and to fundamentally change the field of AI by integrating a wide range of disciplines and prioritizing true diversity of thought. Researchers in Korea analyzed literature evaluating 516 AI algorithms for medical image analysis and found that only 6% validated their AI and 0% were ready for clinical use. This lack of appropriate clinical validation is referred to as digital exceptionalism. An analysis of 47 biomedical unicorns found that most of the highest valued startups in healthcare have a limited or non‐existent participation in the publicly available scientific literature.

How startups are leveraging deep tech knowledge to power ahead


Geeta Manjunath turned entrepreneur in the backdrop of a tragedy. In 2017, a cousin she was really close to succumbed to breast cancer at a relatively young age. Breast cancer is the most commonly occurring cancer in women and the second most common worldwide. Gopinath, who has a PhD in computer science from the Indian Institute of Science, applied her scientific mind to the issue. Ubiquitous screening and early detection vastly reduces fatality from cancer.

Artificial Intelligence May Hold Promise for Early Identification of Cervical Cancer in Women


Researchers from the National Institutes of Health (NIH) and Global Good have created a computer algorithm capable of identifying precancerous changes in women which place them at risk of developing cervical cancer. Known as automated visual evaluation, this new form of artificial intelligence (AI), "has the potential to revolutionize cervical cancer screening" for women in low income communities worldwide by giving their healthcare providers the ability to use digitized images collected during routine, annual screenings for cervical cancer to identify potential precancerous changes. According to America's National Cancer Institute (which is part of the NIH), this technology holds the promise of enabling physicians to more quickly catch and treat such potential changes before they develop into cancer, and could eventually replace visual inspection with acetic acid (VIA) -- the current method of screening used by healthcare professionals who work with limited resources in challenging medical care environments -- a testing system which is "known to be inaccurate." The researchers involved in this project "trained" the machine learning algorithm (automated visual evaluation) to recognize patterns in medical images and other "complex visual inputs" by digitizing and entering more than 60,000 images from an NCI archive of photographs which had been collected from more than 9,400 women in Costa Rica during a 1990s cervical cancer screening study which included follow-up studies for roughly 18 years. These images subsequently enabled the algorithm to "learn" which "cervical changes became precancers and which did not," according to NIH representatives, who added that the AI approach to cervical cancer screening was developed by NCI researchers in collaboration with the Intellectual Ventures Fund, Global Good, with findings confirmed independently by personnel from the National Library of Medicine (NLM), another component of the NIH.

Natural language processing helps hospital predict downstream demand for imaging services


The authors said an automated method for predicting future imaging resource utilization could help streamline the process, paving the way for capacity management strategies that could help meet the increased but unpredictable demand for radiology services. Using data from all hepatocellular carcinoma (HCC) surveillance CT exams performed at their hospital between 2010 and 2017, they used open-source NLP and machine learning software to parse free-text radiology reports into bag-of-words and term frequency-inverse document frequency (TF-IDF) models. In NLP, bag-of-words refers to the frequency with which words occur in a report summary, while TF-IDF considers the number of times a word appears in the summary and measures the uniqueness of specific terms in the context of entire report collections. Brown and Kachura also used three machine learning techniques--logistic regression, support vector machine (SVM) and random forest--to make their predictions. As a whole, the authors found bag-of-words models were somewhat inferior to the TF-IDF approach, with the TF-IDF and SVM combination yielding the most favorable results.

Researchers create nano-bot to probe inside human cells


University of Toronto Engineering researchers have built a set of magnetic'tweezers' that can position a nano-scale bead inside a human cell in three dimensions with unprecedented precision. The nano-bot has already been used to study the properties of cancer cells, and could point the way toward enhanced diagnosis and treatment. Professor Yu Sun and his team have been building robots that can manipulate individual cells for two decades. Their creations have the ability to manipulate and measure single cells--useful in procedures such as in vitro fertilization and personalized medicine. Their latest study, published today in Science Robotics, takes the technology one step further.