Focus |

Digital Medicine

As Nature Medicine celebrates its 25th anniversary, we bring you a special Focus on Digital Medicine that highlights the new technologies transforming medicine and healthcare, as well as the related regulatory challenges ahead.

Reviews and Perspectives

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.

Comment | | Nature Medicine

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.

Comment | | Nature Medicine

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

Perspective | | Nature Medicine

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.

Review Article | | Nature Medicine

News

As Nature Medicine celebrates its 25th anniversary, we bring you a special Focus on Digital Medicine that highlights the new technologies transforming medicine and healthcare, as well as the related regulatory challenges ahead.

Editorial | | Nature Medicine

Genetics and electronic health records come together to identify heritable traits

News Feature | | Nature Medicine

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.

Turning Points | | Nature 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.

Turning Points | | Nature Medicine

Research

Related content

AI is used increasingly in medical diagnostics. Here, the authors present a deep learning model that masters medical knowledge, demonstrated by it having passed the written test of the 2017 National Medical Licensing Examination in China, and can provide help with clinical diagnosis based on electronic health care records.

Article | Open Access | | Nature Communications

Selection of the right cancer treatment is still a challenge. Here, the authors introduce a framework to analyze treatment benefits, using the idea that patients with similar genetic tumor profiles receiving different treatments can be used to model their responses to the alternative treatment.

Article | Open Access | | Nature Communications

Anemia has a global prevalence of over 2 billion people and is diagnosed via blood-based laboratory test. Here the authors describe a smartphone app that can estimate hemoglobin levels and detect anemia by analyzing pictures of fingernail beds taken with a smartphone and without the need of any external equipment.

Article | Open Access | | Nature Communications

In this Review, Yacoub and McLeod summarize the rationale for monitoring patients with heart failure or pulmonary arterial hypertension to detect haemodynamic changes that predict the deterioration from subclinical to overt disease, the transition from noninvasive to implantable devices and the current and anticipated clinical use of these devices.

Review Article | | Nature Reviews Cardiology

Computational models are increasingly used in cardiology to integrate multiple data sets from individual patients and create virtual-patient simulations. In this Review, Niederer and colleagues discuss how multi-scale models of cardiac electrophysiology and mechanics can support diagnostic assessment and clinical decision-making and pave the way to personalized cardiac care.

Review Article | | Nature Reviews Cardiology

Mental health technologies, such as apps, clinical texting, social media platforms and web-based tools, have arrived. Channelling these resources to help people with serious mental illnesses, clinicians in need of support, and people in low-and middle-income countries will have the most impact on the global burden of mental illness.

Comment | | Nature Human Behaviour

Artificial intelligence outperforms traditional statistical models at predicting a range of clinical outcomes from a patient’s entire raw electronic health record (EHR). A team led by Alvin Rajkomar and Eyal Oren from Google in Mountain View, California, USA, developed a data processing pipeline for transforming EHR files into a standardized format. They then applied deep learning models to data from 216,221 adult patients hospitalized for at least 24 h each at two academic medical centers, and showed that their algorithm could accurately predict risk of mortality, hospital readmission, prolonged hospital stay and discharge diagnosis. In all cases, the method proved more accurate than previously published models. The authors provide a case study to serve as a proof-of-concept of how such an algorithm could be used in routine clinical practice in the future.

Article | Open Access | | npj Digital Medicine