• Skip to primary navigation
  • Skip to main content
  • Skip to footer
Longevity & Healthy Aging Research Consortium

Longevity & Healthy Aging Research Consortium

Longevity & Healthy Aging Research Center

Logevity and Healthy Aging
  • Home
  • About Us
    • Contact Us
  • Leadership
  • Education
    • Research Methodology Podcasts
    • Webinar: Receiver Operating Characteristic Curve (ROC) Analysis for Prediction Studies
    • Webinar: Moderator, Mediators et al: Toward Understanding Heterogeneity
  • Publications
  • News and Accomplishments
  • How to Help

Research Methodology Podcasts

Covariates

Last’s Dictionary of Epidemiology says that a covariate is a variable that is possibly predictive of the outcome under study. Even that is somewhat wrong because the word predictive means that the covariate has to occur in time before the outcome that is predicted, but covariates are frequently used when the covariate follows the outcome, when it’s coincident with the outcome as well as when it proceeds the outcome.

To learn more from Dr. Helena Kraemer listen to the podcast episode below.

Click here for more Research Methodology Podcasts

Submit your Questions here

Share this:

  • Click to share on Facebook (Opens in new window) Facebook
  • Click to share on X (Opens in new window) X
Previous Post: « Mediators And Moderators
Next Post: Randomized Clinical Trials »

Longevity Medicine and Healthy Aging

Connect with us

  • Facebook
  • LinkedIn
  • Twitter

Footer

Stanford Medicine

  • About
  • Contact
  • Maps & Directions
  • Careers

 

  • Basic Science Departments
  • Clinical Science Departments
  • Academic Programs

Healthcare

  • Stanford Health Care
  • Stanford Children’s Health
  • Clinical Trials
  •   Find People
  •   Visit Stanford
  •   Search Clinical Trials
  •   Give a Gift

Copyright © 2025 Stanford Medicine
Privacy Policy | Terms of Use

Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).