Predictive medicine

Predictive medicine is a branch of medicine that aims to identify patients at risk of developing a disease, thereby enabling either prevention or early treatment of that disease. Either single or more commonly multiple analyses are used to identify markers of future disposition to a disease.


Latest Research and Reviews

News and Comment

  • News & Views |

    The field of rheumatology is poised to categorize the phenotypes of systemic autoimmune diseases on the basis of measurable and quantifiable molecular signatures. Emerging efforts to identify similarities across diseases, predict clinical outcomes and predict response to therapy using quantitative, data-driven approaches could considerably change treatment paradigms.

    • Michael L. Whitfield
  • Comments & Opinion |

    Many widely used health algorithms have been shown to encode and reinforce racial health inequities, prioritizing the needs of white patients over those of patients of color. Because automated systems are becoming so crucial to access to health, researchers in the field of artificial intelligence must become actively anti-racist. Here we list some concrete steps to enable anti-racist practices in medical research and practice.

    • Kellie Owens
    •  & Alexis Walker
    Nature Medicine 26, 1327-1328
  • News & Views |

    Researchers have developed an in silico (computer) platform that couples tissue adaptation with cellular and molecular interactions to simulate bone adaptation to mechanical loading and progress and treatment of metabolic bone diseases. What is the benefit of such in silico tools, and how can credibility of the simulation outcomes be established?

    • Liesbet Geris
  • Comments & Opinion |

    Testing drug safety in people who are pregnant remains a wicked problem, but in the transition toward big data and machine learning, target trials could afford a viable alternative to randomized, controlled trials.

    • Anup P. Challa
    • , Robert R. Lavieri
    •  & David M. Aronoff
    Nature Medicine 26, 820-821
  • Comments & Opinion |

    Although examples of algorithms designed to improve healthcare delivery abound, for many, clinical integration will not be achieved. The deployment cost of machine learning models is an underappreciated barrier to success. Experts propose three criteria that, assessed early, could help estimate the deployment cost.

    • Keith E. Morse
    • , Steven C. Bagley
    •  & Nigam H. Shah
    Nature Medicine 26, 18-19