Clinicians often prefer to use their clinical judgement instead of relying on statistical algorithms – a phenomenon also observed in other domains of decision making. This review explores this phenomenon using insights from the advice taking literature. One of the most consistent findings of this literature is egocentric advice discounting – the idea that people place more weight on their own judgement than on advice. Advice taking studies typically use the Judge-Advisor System (JAS) to measure how people weigh advice. In JAS studies, participants’ estimates are elicited before and after advice is provided. The shift in estimates is often expressed numerically relative to the advice distance , i.e., the difference between initial estimates and advice, using the Weight-of-Advice index. Although advice taking studies have traditionally used lay participants making everyday judgements of low stakes, more recent research has used experts delivering judgements on domain-relevant problems.

The review also discusses the popular concept of algorithm aversion , that is, preference for human over algorithmic advice due to intolerance of algorithmic error and comparatively greater acceptance of human error. This concept is not always borne out in research and studies have also observed preference for algorithmic advice. Insights for medical decision making are drawn, although the direct comparison of preference for human vs. algorithmic advice has not yet been investigated in clinicians.

Ways to increase algorithmic advice uptake are discussed. Finally, the neglected idea that advice can be used to support learning is discussed with evidence from a range of studies. In an era rife with calls for machines to replace doctors, shifting emphasis from AI-bots to AI-tutors could promote uptake and reduce fears of professional deskilling and automation bias.