Collection |

Machine learning in healthcare

The accelerating power of machine learning in diagnosing disease and in sorting and classifying health data will empower physicians and speed-up decision making in the clinic.

This Collection is updated when relevant new content is published. Content appears in reverse chronological order. See all Collections from Nature Biomedical Engineering.

Research

  • Nature Biomedical Engineering | Article

    An interpretable deep-learning algorithm trained on a small dataset of computed-tomography scans of the head detects acute ICH and classifies the pathology subtypes, with a performance comparable to expert radiologists.

    • Hyunkwang Lee
    • , Sehyo Yune
    • , Mohammad Mansouri
    • , Myeongchan Kim
    • , Shahein H. Tajmir
    • , Claude E. Guerrier
    • , Sarah A. Ebert
    • , Stuart R. Pomerantz
    • , Javier M. Romero
    • , Shahmir Kamalian
    • , Ramon G. Gonzalez
    • , Michael H. Lev
    •  &  Synho Do
  • Nature Biomedical Engineering | Review Article

    This Review summarizes the medical applications of artificial intelligence, and its economic, legal and social implications for healthcare.

    • Kun-Hsing Yu
    • , Andrew L. Beam
    •  &  Isaac S. Kohane
  • Nature Biomedical Engineering | Article

    An alert system based on machine learning and trained on surgical data from electronic medical records helps anaesthesiologists prevent hypoxaemia during surgery by providing interpretable real-time predictions.

    • Scott M. Lundberg
    • , Bala Nair
    • , Monica S. Vavilala
    • , Mayumi Horibe
    • , Michael J. Eisses
    • , Trevor Adams
    • , David E. Liston
    • , Daniel King-Wai Low
    • , Shu-Fang Newman
    • , Jerry Kim
    •  &  Su-In Lee
  • Nature Biomedical Engineering | Article

    A deep-learning algorithm can detect polyps in the colon in real time and with high sensitivity and specificity, according to validation studies with prospectively collected images and videos from colonoscopies performed in 1,138 patients.

    • Pu Wang
    • , Xiao Xiao
    • , Jeremy R. Glissen Brown
    • , Tyler M. Berzin
    • , Mengtian Tu
    • , Fei Xiong
    • , Xiao Hu
    • , Peixi Liu
    • , Yan Song
    • , Di Zhang
    • , Xue Yang
    • , Liangping Li
    • , Jiong He
    • , Xin Yi
    • , Jingjia Liu
    •  &  Xiaogang Liu
  • Nature Biomedical Engineering | Article

    An assay that uses machine-learning algorithms on phenotypic-biomarker data from live primary cells predicts post-surgical adverse pathology in prostate-cancer and breast cancer tissue samples from patients.

    • Michael S. Manak
    • , Jonathan S. Varsanik
    • , Brad J. Hogan
    • , Matt J. Whitfield
    • , Wendell R. Su
    • , Nikhil Joshi
    • , Nicolai Steinke
    • , Andrew Min
    • , Delaney Berger
    • , Robert J. Saphirstein
    • , Gauri Dixit
    • , Thiagarajan Meyyappan
    • , Hui-May Chu
    • , Kevin B. Knopf
    • , David M. Albala
    • , Grannum R. Sant
    •  &  Ashok C. Chander
  • Nature Biomedical Engineering | Article

    A low-cost point-of-care device that uses contrast-enhanced microholography and deep learning accurately detects aggressive lymphomas in patients referred for aspiration and biopsy of enlarged lymph nodes.

    • Hyungsoon Im
    • , Divya Pathania
    • , Philip J. McFarland
    • , Aliyah R. Sohani
    • , Ismail Degani
    • , Matthew Allen
    • , Benjamin Coble
    • , Aoife Kilcoyne
    • , Seonki Hong
    • , Lucas Rohrer
    • , Jeremy S. Abramson
    • , Scott Dryden-Peterson
    • , Lioubov Fexon
    • , Misha Pivovarov
    • , Bruce Chabner
    • , Hakho Lee
    • , Cesar M. Castro
    •  &  Ralph Weissleder

News & Comment

  • Nature Biomedical Engineering | Editorial

    Clinical implementations of machine learning that are accurate, robust and interpretable will eventually gain the trust of healthcare providers and patients.

  • Nature Biomedical Engineering | News & Views

    A deep-learning algorithm enables the real-time video-based recognition of polyps during colonoscopy, with sensitivities and specificities surpassing 90%.

    • Yuichi Mori
    •  &  Shin-ei Kudo
  • Nature Biomedical Engineering | News & Views

    A holographic approach relying on small-molecule chromogens enables a rapid and inexpensive test for the accurate classification of aggressive lymphoma at the point of care.

    • Varun L. Kopparthy
    • , Ryan Snodgrass
    •  &  David Erickson
  • Nature Biomedical Engineering | News & Views

    A microfluidic device for assaying neutrophil motility in blood samples from sepsis patients and a machine-learning algorithm trained with the motility data enable a faster and accurate sepsis diagnosis.

    • Umer Hassan
    • , Enrique Valera
    •  &  Rashid Bashir
  • Nature Biomedical Engineering | Editorial

    Artificial intelligence may eventually help diagnose eye conditions and the risk of cardiovascular disease, solely from retinal images.