AI and machine learning are playing an important role in fighting the pandemic brought on by COVID-19, with technological innovation and ingenuity being applied to large volumes of data to quickly identify patterns and gain insights. Efforts are underway to speed up research and treatment, and better understand how COVID-19 spreads. Chatbots employing AI are speeding up communication around the pandemic. One example is from Clevy.io, a French startup that launched a chatbot to make it easier for people to find official government communications about COVID-19, according to an account from the World Economic Forum. The bot is getting realtime information from the French government and the World Health Organization, to help relay known symptoms and answer questions about government policies.

Mount Sinai Hospital last year switched on an artificial intelligence program to search the hospital's records for evidence of malnourished patients in its wards. NEW YORK -- Last year, Mount Sinai Hospital switched on an artificial intelligence program to search the hospital's records for evidence of malnourished patients in its wards. The numbers it turned up were eye-popping: 20 percent more cases were diagnosed than in the previous year. Around the same time, Barbara Murphy, chief of the renowned health system's department of medicine, was helping to develop another AI program, to predict whether diabetic patients are at near-term risk of kidney disease and to help prioritize specialist visits for those who are. One of the early findings, according to Murphy: "We probably need some more nephrologists."

Artificial intelligence (AI) tools trained to detect pneumonia on chest X-rays suffered significant decreases in performance when tested on data from outside health systems, according to a study conducted at the Icahn School of Medicine at Mount and published in a special issue of PLOS Medicine on machine learning and health care. These findings suggest that artificial intelligence in the medical space must be carefully tested for performance across a wide range of populations; otherwise, the deep learning models may not perform as accurately as expected. As interest in the use of computer system frameworks called convolutional neural networks (CNN) to analyze medical imaging and provide a computer-aided diagnosis grows, recent studies have suggested that AI image classification may not generalize to new data as well as commonly portrayed. Researchers at the Icahn School of Medicine at Mount Sinai assessed how AI models identified pneumonia in 158,000 chest X-rays across three medical institutions: the National Institutes of Health; The Mount Sinai Hospital; and Indiana University Hospital. Researchers chose to study the diagnosis of pneumonia on chest X-rays for its common occurrence, clinical significance, and prevalence in the research community.

The New York-based medical school founded by Mount Sinai Hospital has launched a new research center focused on blockchain applications in healthcare. On Tuesday, The Icahn School of Medicine said the Center for Biomedical Blockchain Research would be created inside the school's Institute for Next Generation Healthcare, which researches the application of artificial intelligence, robotics, genomic sequencing, sensors and wearable devices in medicine, New York-based news organization Crain's reported. The center's staff will conduct academic research on blockchain in medicine, as well as create their own prototype networks. The possible use cases include drug development and preventing the sale of counterfeit drugs, clinical trials and a better research reproducibility, Healthcare IT News wrote. The new center will be run by Joel Dudley, executive vice president of Precision Health at Mount Sinai and a former senior data scientist at Pivotal Software, which researches the use of artificial intelligence in biology.

Computer algorithms can automatically interpret echocardiographic images and distinguish between pathological hypertrophic cardiomyopathy (HCM) and physiological changes in athletes' hearts, according to research from the Icahn School of Medicine at Mount Sinai (ISMMS), published online yesterday in the Journal of the American College of Cardiology. HCM is a disease in which a portion of the myocardium enlarges, creating functional impairment of the heart. It is the leading cause of sudden death in young athletes. Diagnosing HCM is challenging since athletes can present with physiological hypertrophy, in which their hearts appear large, but do not feature the pathological abnormality of HCM. The current standard of care requires precise phenotyping of the two similar conditions by a highly trained cardiologist.

The Department of Pathology at the Icahn School of Medicine at Mount Sinai has established the Center for Computational and Systems Pathology to revolutionize pathology practice, using advanced computer science and mathematical techniques coupled with cutting-edge microscope technology and artificial intelligence. The goal of this new academic research facility is to explore efforts to more accurately classify diseases and guide treatment using computer vision and machine learning techniques. The Center for Computational and Systems Pathology will be a hub for the development of new diagnostic, predictive, and prognostic tests and will partner with Mount Sinai-based "Precise Medical Diagnostics" (Precise MDTM), which has been under development for more than three years by a team of physicians, scientists, mathematicians, engineers, and programmers. Carlos Cordon-Cardo, MD, PhD, will oversee the new center, located at Mount Sinai St. Luke's, and will continue his role as Chair of the Department of Pathology at the Mount Sinai Health System and Professor of Pathology, Genetics and Genomic Sciences, and Oncological Sciences at the Icahn School of Medicine. Gerardo Fernandez, MD, Associate Professor of Pathology, and Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, will be the Center's Medical Director.