Could janus kinase (JAK) inhibitors like Eli Lilly's rheumatoid arthritis drug Olumiant be repurposed to treat Alzheimer's disease? Researchers at Harvard University and Massachusetts General Hospital have set out to find the answer to that question with a new clinical trial that was born from artificial intelligence. The researchers used a type of AI called machine learning to identify existing drugs that might be able to prevent neuronal death in Alzheimer's. The screen pulled up a list of 15 FDA-approved drugs as candidates for repurposing in Alzheimer's, and five of them were JAK inhibitors, they reported in the journal Nature Communications. JAK proteins fuel inflammation and have long been suspected to play a role in Alzheimer's.

Mass General's Constance ("Connie") Lehman, MD, PhD, is chief of Breast Imaging, Professor of Radiology at Harvard Medical School, and co-director of the Avon Comprehensive Breast Evaluation Center. Below, she describes her collaboration with colleagues from MIT's Computer Science and Artificial Intelligence Laboratory and their pioneering work developing an image-based model that predicts breast cancer up to five years in advance. Why is it important to have better breast cancer risk assessment tools? Most women diagnosed with breast cancer have no "known" risk factors other than being female. We knew if we could develop better methods to assess a woman's personal risk of breast cancer, we could redesign our screening programs, tailored to each individual woman's risk.

Radiologists at Mass General are using AI to begin to analyze lung damage data from COVID-19 to predict the best treatment for patients. Nvidia DGX A100 accelerators are helping the task, which involves using X-ray images of lungs to be combined with radiology data from other clinical insights to predict outcomes for COVID patients, according to an Nvidia blog. Mass General Brigham used its own data to build the models. Once validated, they could be deployed in a hospital setting to track patient progress and offer treatment insights. Matthew D. Li, a radiology resident at Mass General and a member of the Martino Center QTIM Lab said there's information in radiologic images not available to doctors as they make treatment plans.

Advances in AI and Machine Learning are adding an unprecedented level of intelligence to everything through capabilities such as speech …

Already, at Massachusetts General Hospital in Boston, "every one of the 50,000 screening mammograms we do every year is processed through our deep learning model, and that information is provided to the radiologist," says Constance Lehman, chief of the hospital's breast imaging division. In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data. The raw power of the technology has improved dramatically in recent years, and it's now used in everything from medical diagnostics to online shopping to autonomous vehicles. But deep learning tools also raise worrying questions because they solve problems in ways that humans can't always follow. If the connection between the data you feed into the model and the output it delivers is inscrutable -- hidden inside a so-called black box -- how can it be trusted?

In clinics around the world, a type of artificial intelligence called deep learning is starting to supplement or replace humans in common tasks such as analyzing medical images. Already, at Massachusetts General Hospital in Boston, "every one of the 50,000 screening mammograms we do every year is processed through our deep learning model, and that information is provided to the radiologist," says Constance Lehman, chief of the hospital's breast imaging division. In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data. The raw power of the technology has improved dramatically in recent years, and it's now used in everything from medical diagnostics to online shopping to autonomous vehicles. But deep learning tools also raise worrying questions because they solve problems in ways that humans can't always follow.

Healthcare continued to be a lucrative target for hackers in 2017 with weaponized ransomware, misconfigured cloud storage buckets and phishing emails dominating the year. In 2018, these threats will continue and cybercriminals will likely get more creative despite better awareness among healthcare organizations at the executive level for the funding needed to protect themselves.

Artificial intelligence is beginning to reshape healthcare and life sciences. And one application of AI, deep learning, is coming into its own. Deep learning is a type of machine learning based on data representations rather than task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Much of the excitement around AI today is fundamental because of three ingredients: the development of algorithms that make artificial neural networks, the increasing supply of digital data that now can be created, and, critically, the "GPU" chip architecture – it stands for graphics processing unit – pioneered by vendor NVIDIA, said Mark Michalski, MD, executive director of the Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science. "GPU chips are different than the CPU chips that run many of our computers today in that they solve many simple problems simultaneously, as opposed to one big problem at a time, like CPUs," Michalski explained.

In a Journal of Digital Imaging paper published online in March 2017, researchers at Massachusetts General Hospital described a deep learning system for bone age assessment that addresses this limitation and provides a fully automated approach for clinical implementation. Here, AI infers the bone age from an X-ray image with no other patient information, trained by a convolutional neural network (CNN) using a data set including age and associated ideal X-rays. A CNN is a class of deep learning networks that mimics the neural connectivity patterns found in the animal visual cortex. It can provide confidence level and predicted age for any X-ray within seconds. The researchers tested the new deep learning system by applying it to more than 10,000 radiographs obtained at Mass General between 2005 and 2015.

Massachusetts General Hospital is buying into deep learning artificial intelligence, and it will use Nvidia's new DGX-1 deep-learning supercomputer that was announced today. Nvidia is partnering with the MGH Clinical Data Science Center, which wants to advance health care with AI to improve the detection, diagnosis, treatment, and management of diseases. "Deep learning is revolutionizing a wide range of scientific fields," said Jen-Hsun Huang, CEO of Nvidia, at the company's GPUTech event in San Jose, California, today. "There could be no more important application of this new capability than improving patient care. Massachusetts General Hospital runs the largest hospital-based research program in the United States, and is the top-ranked hospital on this year's U.S. News and World Report's "Best Hospitals" list. The center will train a deep neural network using Mass General's vast stores of phenotypic, genetics, and imaging data. The hospital has a database containing some 10 billion medical images. To do this, it will use the Nvidia DGX-1 -- a supercomputer designed for AI applications. Using AI, physicians can compare a patient's symptoms, tests, and history with insight from a vast population of other patients. Initially, the MGH Clinical Data Science Center will focus on the fields of radiology and pathology -- which are particularly rich in images and data -- and then expand into genomics and electronic health records. "We now have the ability to expand the field of radiology beyond its predominant state of providing visualization for human interpretation," said Keith J. Dreyer, vice chairman of Radiology at Mass General and executive director of the center, in a statement. "Guided by precision healthcare, we are entering the radiological era of biometric quantification, where our interpretations will be enhanced by algorithms learned from the diagnostic data of vast patient populations.