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Can we train better medical intuition? Exploring the potential of debiasing interventions
Nina Franiatte1, Wim De Neys2, Esther Boissin3
1Université Paris Cité, LaPsyDÉ, CNRS, Paris, France; Research and Development Team, Onepoint, Bègles, France.
None:
Human judgment is often prone to biases, and healthcare professionals are no exception. In clinical environments - characterized by high pressure, time constraints, and information overload - intuitive impressions can sometimes override statistical reasoning, leading to severe consequences such as diagnostic errors. There is an urgent need to identify effective strategies for reducing clinical decision-making errors and improving patient safety. In this paper, we examine the roots of logical fallacies and present promising debiasing interventions designed to mitigate such biases. Especially, we review debiasing training procedures that have been shown to significantly boost logico-mathematical reasoning, producing durable improvements across reasoning tasks and populations. Critically, these improvements occur as early as the initial intuitive stage, allowing faster and more accurate responses - which is particularly relevant to clinical contexts. We also discuss recent alternative procedures that help identify the conditions under which training is most effective. Overall, these findings suggest that short debiasing training can cultivate reliable intuitive judgments, offering a scalable, ecologically valid path to reducing medical decision-making errors.
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