Babakov, NikolaySivaprasad, AdarsaReiter, EhudBugarín-Diz, Alberto2025-05-052025-05-052025-02-07Babakov, N., Sivaprasad, A., Reiter, E. et al. Reusability of Bayesian Networks case studies: a survey. Appl Intell 55, 417 (2025). https://doi.org/10.1007/s10489-025-06289-51573-74970924-669Xhttps://hdl.handle.net/10347/41174Bayesian Networks (BNs) are probabilistic graphical models used to represent variables and their conditional dependencies, making them highly valuable in a wide range of fields, such as radiology, agriculture, neuroscience, construction management, medicine, and engineering systems, among many others. Despite their widespread application, the reusability of BNs presented in papers that describe their application to real-world tasks has not been thoroughly examined. In this paper, we perform a structured survey on the reusability of BNs using the PRISMA methodology, analyzing 147 papers from various domains. Our results indicate that only 18% of the papers provide sufficient information to enable the reusability of the described BNs. This creates significant challenges for other researchers attempting to reuse these models, especially since many BNs are developed using expert knowledge elicitation. Additionally, direct requests to authors for reusable BNs yielded positive results in only 12% of cases. These findings underscore the importance of improving reusability and reproducibility practices within the BN research community, a need that is equally relevant across the broader field of Artificial Intelligence.eng©TheAuthor(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Bayesian networksProbabilistic graphical modelsReproducibilityReusability1209 Estadísticajournal article10.1007/s10489-025-06289-5open access