Volume 25 Issue 1, January 2019

Exploring the evolution, from analysis and endpoints to registration and regulations

Genetics and electronic health records come together to identify heritable traits

Machine learning makes new sense of psychiatric symptoms

Rima Arnaout is an assistant professor of cardiology and a member of the University of California San Francisco Bakar Computational Health Sciences Institute. She has received a Chan Zuckerberg Biohub Intercampus Research Award, as well as funding support from the US National Institutes of Health and the American Heart Association’s Institute for Precision Cardiovascular Medicine.

Kee Yuan Ngiam is the group chief technology officer at National University Health System, Singapore, and assistant professor at the School of Medicine of the National University of Singapore. His research focuses on the effects of using artificial intelligence in healthcare. He is the 2018 recipient of Singapore’s National Health IT Excellence Award, which recognizes individuals who advanced healthcare through innovation.

Here we argue that now is the time to create smarter healthcare systems in which the best treatment decisions are computationally learned from electronic health record data by deep-learning methodologies.

In this Comment, we provide guidelines for reinforcement learning for decisions about patient treatment that we hope will accelerate the rate at which observational cohorts can inform healthcare practice in a safe, risk-conscious manner.

An exercise-linked hormone, FNDC5/irisin, mediates the benefit of exercise in Alzheimer’s disease models by enhancing synaptic plasticity and memory.

Deep-learning algorithms can be applied to large datasets of electrocardiograms, are capable of identifying abnormal heart rhythms and mechanical dysfunction, and could aid healthcare decisions.

A primer for deep-learning techniques for healthcare, centering on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods.

To bring the full potential of AI to the clinic, practical and regulatory changes need to be made in health systems globally.

The increased amount of health care data collected brings with it ethical and legal challenges for protecting the patient while optimizing health care and research.

Artificial intelligence is beginning to be applied in the medical setting and has potential to improve workflows and errors, impacting patients and clinicians alike.

An algorithm trained on half a million electronic medical records predicts chronic kidney disease in diabetic patients using a small set of defined clinical features, outperforming predictions derived from clinical trial data.

A deep-learning algorithm, trained on over 17,000 real-world patient facial images, achieves high accuracy in identifying rare genetic disorders.

Analysis of electrocardiograms using an end-to-end deep learning approach can detect and classify cardiac arrhythmia with high accuracy, similar to that of cardiologists.

A deep learning algorithm applied to the electrocardiogram—a test of the heart’s electrical activity—can detect abnormally low contractile function of the heart, opening up the possibility for a simple screening tool for this condition.

A new population of T helper cells employs unique signals to support autoreactive B cells in patients with systemic lupus erythematosus.

A novel chimeric antigen receptor (CAR) T cell design improves therapeutic efficacy by balancing effector and memory profiles.

Intratumoral T cells that are capable of recognizing adjacent tumor tissue antigens can be exceedingly rare.

RNA editing enzyme ADAR1 is a therapeutic vulnerability in tumors with constitutive cell-autonomous production of interferon-stimulated genes

High-resolution sequencing of longitudinal patient samples reveals subclonal mutational diversity in the stem cell compartment driving parallel evolution patterns in acute myeloid leukemia progression.

Nongenetic activation of Aurora kinase A in the majority of patients with non-small-cell lung cancer mediates adaptive resistance to EGFR inhibition and offers an opportunity for combination treatment.

Molecular alterations in ctDNA of patients with mantle cell lymphoma uncovers mutations in SWI–SNF associated with resistance to ibrutinib and venetoclax combination and provide a rationale for restoring sensitivity through Bcl-xL inhibition.

WASP is a novel tumor-suppressor gene in ALK-driven anaplastic large cell lymphoma through modulation of CDC42 and MAPK signaling and provides a rationale for combination therapy.

Obesity promotes tumor growth yet simultaneously increases susceptibility to PD-1-targeted cancer immunotherapy.

Evolutionarily conserved gene modules mediate neurodegenerative dementia in a variety of mouse models and in postmortem brain tissue from patients.

Expression of the exercise-induced myokine irisin (FNDC5) is lower in patients with AD. Whereas knockdown of FNDC5/irisin is sufficient to induce learning and memory deficits, restoration of its expression can ameliorate these phenotypes in rodent models.

An integrated analysis of glioma samples from patients with neurofibromatosis 1 annotates their mutational, epigenetic, transcriptional, and immunological features and uncovers similitudes with a subset of sporadic gliomas.