The Role of Bayesian Approaches in Medical Education for Enhancing Evidence-Based Medicine.
Abstract
The increasing complexity of clinical decision-making in evidence-based medicine (EBM) highlights the need for probabilistic reasoning. Bayesian theory provides a structured framework to integrate prior knowledge with new evidence, yet its use in medical education remains limited. This paper argues that incorporating Bayesian approaches improves diagnostic accuracy and reduces reliance on heuristic judgments. Educational strategies such as case-based learning help bridge theory and practice, while Bayesian methods strengthen the link between research and clinical decision-making. Despite challenges such as perceived complexity and variability in estimating pre-test probabilities, these barriers reflect gaps in pedagogy. We conclude that Bayesian reasoning should be integrated as a core component of medical training to better address clinical uncertainty.
Downloads
-
Abstract0
-
pdf (Español (España))0
-
xml (Español (España))0
-
Infografía (Español (Espa...0
References
1. Kass R, & Raftery A. Bayes Factors. J Am Stat Assoc. 1995, 90(430), 773-795. https://doi.org/10.1080/01621459.1995.10476572
2. Schoot R, Depaoli S, Gelman A, King R, Kramer B, Märtens K, et al. Bayesian statistics and modelling. Nat Rev Methods Primers. 2021, 1(1). https://doi.org/10.1038/s43586-020-00003-0
3. Benbassat J. Teaching Clinical Reasoning to Undergraduate Medical Students. Thinking Like a Policy Analyst. 2005, 53-71. https://doi.org/10.1057/9781403980939_3
4. Brush J, Lee M, Sherbino J, Taylor-Fishwick J, & Norman G. Effect of Teaching Bayesian Methods Using Learning by Concept vs Learning by Example on Medical Students’ Ability to Estimate Probability of a Diagnosis. JAMA Netw Open. 2019, 2(12), e1918023. https://doi.org/10.1001/jamanetworkopen.2019.18023
5. Nelson A. An Interactive Workshop Reviewing Basic Biostatistics and Applying Bayes' Theorem to Diagnostic Testing and Clinical Decision-Making. MedEdPORTAL. 2018, 14, 10771 https://doi.org/10.15766/mep_2374-8265.10771
6. Elstein A. On the origins and development of evidence-based medicine and medical decision making. Inflamm Res. 2004, 53(S2), S184–S189. https://doi.org/10.1007/s00011-004-0357-2
7. Helgason C, & Jobe T. Causality and clinical medicine: Using fuzzy measures for patient prediction and experimental design. NAFIPS Annu Meet. 2008, 1-5. https://doi.org/10.1109/nafips.2008.4531320
8. Cahan A, Gilon D, Manor O, & Paltiel O. Probabilistic reasoning and clinical decision-making: do doctors overestimate diagnostic probabilities?. QJM. 2003, 96(10), 763-769. https://doi.org/10.1093/qjmed/hcg122
9. Phelps M, & Levitt M. Pretest Probability Estimates: A Pitfall to the Clinical Utility of Evidence‐based Medicine?. Acad Emerg Med. 2004, 11(6), 692-694. https://doi.org/10.1197/j.aem.2003.08.022
Copyright (c) 2026 Servicio de Publicaciones de la Universidad de Murcia
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The works published in this magazine are subject to the following terms:
1. The Publications Service of the University of Murcia (the publisher) preserves the economic rights (copyright) of the published works and favors and allows them to be reused under the use license indicated in point 2.
2. The works are published under a Creative Commons Attribution-NonCommercial-NoDerivative 4.0 license.
3. Self-archiving conditions. Authors are allowed and encouraged to disseminate electronically the pre-print versions (version before being evaluated and sent to the journal) and / or post-print (version evaluated and accepted for publication) of their works before publication , since it favors its circulation and earlier diffusion and with it a possible increase in its citation and reach among the academic community.
This is a Diamond Journal 
