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Related Concept Videos

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Related Experiment Videos

Advice taking in medical decision making.

Olga Kostopoulou1

  • 1Imperial College London, Department of Surgery and Cancer, Faculty of Medicine, London, UK.

Presse Medicale (Paris, France : 1983)
|June 2, 2026
PubMed
Summary
This summary is machine-generated.

People often favor their own judgment over advice, a bias seen in clinical and other decision-making. Understanding advice-taking and algorithm aversion can improve the use of AI-tutors in medicine.

Keywords:
Advice takingAlgorithm aversionClinical decision-making

Related Experiment Videos

Area of Science:

  • Decision-making science
  • Cognitive psychology
  • Medical informatics

Background:

  • Clinicians frequently prioritize clinical judgment over statistical algorithms.
  • This preference mirrors a broader decision-making phenomenon known as egocentric advice discounting.
  • Algorithm aversion, or preferring human over algorithmic advice, is a related concept but not universally observed.

Purpose of the Study:

  • To review the advice-taking literature concerning human judgment versus algorithmic input.
  • To explore the concept of algorithm aversion in decision-making contexts.
  • To draw insights for improving algorithmic advice uptake in clinical settings.

Main Methods:

  • Literature review synthesizing findings from advice-taking studies.
  • Analysis of the Judge-Advisor System (JAS) methodology for quantifying advice weighting.
  • Discussion of research on human-computer interaction and decision support.

Main Results:

  • Egocentric advice discounting is a consistent finding, where individuals overvalue their own judgments.
  • Research on algorithm aversion shows mixed results, with some studies indicating preference for algorithmic advice.
  • The potential for advice to support learning, rather than just immediate decision-making, is highlighted.

Conclusions:

  • Shifting focus from AI-bots to AI-tutors could enhance the adoption of algorithmic tools in medicine.
  • Addressing clinician preferences and potential biases is crucial for effective integration of AI in healthcare.
  • Emphasizing AI's role in learning may mitigate fears of job displacement and automation bias.