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

  • Nature Medicine | Comment

    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.

    • Beau Norgeot
    • , Benjamin S. Glicksberg
    •  &  Atul J. Butte
  • Nature Medicine | Comment

    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.

    • Omer Gottesman
    • , Fredrik Johansson
    • , Matthieu Komorowski
    • , Aldo Faisal
    • , David Sontag
    • , Finale Doshi-Velez
    •  &  Leo Anthony Celi
  • Nature Medicine | Perspective

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

    • Andre Esteva
    • , Alexandre Robicquet
    • , Bharath Ramsundar
    • , Volodymyr Kuleshov
    • , Mark DePristo
    • , Katherine Chou
    • , Claire Cui
    • , Greg Corrado
    • , Sebastian Thrun
    •  &  Jeff Dean
  • Nature Medicine | Review Article

    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.

    • W. Nicholson Price II
    •  &  I. Glenn Cohen

News

  • Nature Medicine | Editorial

    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.

  • Nature Medicine | News Feature

    Genetics and electronic health records come together to identify heritable traits

    • Shraddha Chakradhar
  • Nature Medicine | News Feature

    Machine learning makes new sense of psychiatric symptoms

    • Brianna Abbott
  • Nature Medicine | Turning Points

    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.

    • Rima Arnaout
  • Nature Medicine | Turning Points

    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.

    • Kee Yuan Ngiam

Research

  • Nature Medicine | Letter

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

    • Yaron Gurovich
    • , Yair Hanani
    • , Omri Bar
    • , Guy Nadav
    • , Nicole Fleischer
    • , Dekel Gelbman
    • , Lina Basel-Salmon
    • , Peter M. Krawitz
    • , Susanne B. Kamphausen
    • , Martin Zenker
    • , Lynne M. Bird
    •  &  Karen W. Gripp
  • Nature Medicine | Letter

    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.

    • Zachi I. Attia
    • , Suraj Kapa
    • , Francisco Lopez-Jimenez
    • , Paul M. McKie
    • , Dorothy J. Ladewig
    • , Gaurav Satam
    • , Patricia A. Pellikka
    • , Maurice Enriquez-Sarano
    • , Peter A. Noseworthy
    • , Thomas M. Munger
    • , Samuel J. Asirvatham
    • , Christopher G. Scott
    • , Rickey E. Carter
    •  &  Paul A. Friedman
  • Nature Medicine | Article

    A convolutional neural network model using feature extraction and machine-learning techniques provides a tool for classification of lung cancer histopathology images and predicting mutational status of driver oncogenes

    • Nicolas Coudray
    • , Paolo Santiago Ocampo
    • , Theodore Sakellaropoulos
    • , Navneet Narula
    • , Matija Snuderl
    • , David Fenyö
    • , Andre L. Moreira
    • , Narges Razavian
    •  &  Aristotelis Tsirigos
  • Nature Medicine | Letter

    A deep-learning algorithm is developed to provide rapid and accurate diagnosis of clinical 3D head CT-scan images to triage and prioritize urgent neurological events, thus potentially accelerating time to diagnosis and care in clinical settings.

    • Joseph J. Titano
    • , Marcus Badgeley
    • , Javin Schefflein
    • , Margaret Pain
    • , Andres Su
    • , Michael Cai
    • , Nathaniel Swinburne
    • , John Zech
    • , Jun Kim
    • , Joshua Bederson
    • , J. Mocco
    • , Burton Drayer
    • , Joseph Lehar
    • , Samuel Cho
    • , Anthony Costa
    •  &  Eric K. Oermann
  • Nature Medicine | Article

    A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.

    • Jeffrey De Fauw
    • , Joseph R. Ledsam
    • , Bernardino Romera-Paredes
    • , Stanislav Nikolov
    • , Nenad Tomasev
    • , Sam Blackwell
    • , Harry Askham
    • , Xavier Glorot
    • , Brendan O’Donoghue
    • , Daniel Visentin
    • , George van den Driessche
    • , Balaji Lakshminarayanan
    • , Clemens Meyer
    • , Faith Mackinder
    • , Simon Bouton
    • , Kareem Ayoub
    • , Reena Chopra
    • , Dominic King
    • , Alan Karthikesalingam
    • , Cían O. Hughes
    • , Rosalind Raine
    • , Julian Hughes
    • , Dawn A. Sim
    • , Catherine Egan
    • , Adnan Tufail
    • , Hugh Montgomery
    • , Demis Hassabis
    • , Geraint Rees
    • , Trevor Back
    • , Peng T. Khaw
    • , Mustafa Suleyman
    • , Julien Cornebise
    • , Pearse A. Keane
    •  &  Olaf Ronneberger

Related content

  • Nature Communications | Article | open

    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.

    • Ji Wu
    • , Xien Liu
    • , Xiao Zhang
    • , Zhiyang He
    •  &  Ping Lv
  • Nature Communications | Article | open

    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.

    • Joske Ubels
    • , Pieter Sonneveld
    • , Erik H. van Beers
    • , Annemiek Broijl
    • , Martin H. van Vliet
    •  &  Jeroen de Ridder
  • Nature Communications | Article | open

    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.

    • Robert G. Mannino
    • , David R. Myers
    • , Erika A. Tyburski
    • , Christina Caruso
    • , Jeanne Boudreaux
    • , Traci Leong
    • , G. D. Clifford
    •  &  Wilbur A. Lam
  • Nature Reviews Cardiology | Review Article

    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.

    • Magdi H. Yacoub
    •  &  Christopher McLeod
  • Nature Reviews Cardiology | Review Article

    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.

    • Steven A. Niederer
    • , Joost Lumens
    •  &  Natalia A. Trayanova
  • Nature Human Behaviour | Comment

    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.

    • Dror Ben-Zeev
    •  &  David C. Atkins