Focus |

Machine Learning in Medicine

Machine learning has had a significant impact in many areas of science and technology, including life science and medical research. In this Focus issue, we highlight the recent advances that have been made in the development of machine learning algorithms for fundamental aspects such as statistical bioinformatics to their deployment in clinical diagnosis, prognosis and drug development.

Content

  • Nature Materials | Editorial

    Machine learning is swiftly infiltrating many areas within the healthcare industry, from diagnosis and prognosis to drug development and epidemiology, with significant potential to transform the medical landscape.

  • Nature Materials | Comment

    Rapid progress in machine learning is enabling opportunities for improved clinical decision support. Importantly, however, developing, validating and implementing machine learning models for healthcare entail some particular considerations to increase the chances of eventually improving patient care.

    • Po-Hsuan Cameron Chen
    • , Yun Liu
    •  &  Lily Peng
  • Nature Materials | Comment

    Highly quantitative, robust, single-cell analyses can help to unravel disease heterogeneity and lead to clinical insights, particularly for complex and chronic diseases. Advances in computer vision and machine learning can empower label-free cell-based diagnostics to capture subtle disease states.

    • Minh Doan
    •  &  Anne E. Carpenter
  • Nature Materials | Comment

    At the recent Artificial Intelligence Applications in Biopharma Summit in Boston, USA, a panel of scientists from industry who work at the interface of machine learning and pharma discussed the diverging opinions on the past, present and future role of AI for ADME/Tox in drug discovery and development.

    • Barun Bhhatarai
    • , W. Patrick Walters
    • , Cornelis E. C. A. Hop
    • , Guido Lanza
    •  &  Sean Ekins
  • Nature Materials | Comment

    This Comment describes some of the common pitfalls encountered in deriving and validating predictive statistical models from high-dimensional data. It offers a fresh perspective on some key statistical issues, providing some guidelines to avoid pitfalls, and to help unfamiliar readers better assess the reliability and significance of their results.

    • Andrew E. Teschendorff
  • Nature Materials | Perspective

    This Perspective describes the application of machine learning models in the design, synthesis and characterisation of molecules at different stages in the drug discovery and development process.

    • Sean Ekins
    • , Ana C. Puhl
    • , Kimberley M. Zorn
    • , Thomas R. Lane
    • , Daniel P. Russo
    • , Jennifer J. Klein
    • , Anthony J. Hickey
    •  &  Alex M. Clark