  Background: Bayesian Networks (BNs) are probabilistic graphical models that
leverage Bayes' theorem to portray dependencies and cause-and-effect
relationships between variables. These networks have gained prominence in the
field of health sciences, particularly in diagnostic processes, by allowing the
integration of medical knowledge into models and addressing uncertainty in a
probabilistic manner.
  Objectives: This review aims to provide an exhaustive overview of the current
state of Bayesian Networks in disease diagnosis and prognosis. Additionally, it
seeks to introduce readers to the fundamental methodology of BNs, emphasizing
their versatility and applicability across varied medical domains.
  Methods: Employing a meticulous search strategy with MeSH descriptors in
diverse scientific databases, we identified 190 relevant references. These were
subjected to a rigorous analysis, resulting in the retention of 60 papers for
an in-depth review. The robustness of our approach minimizes the risk of
selection bias. Results: The selected studies encompass a wide range of medical
areas, providing insights into the statistical methodology, implementation
feasibility, and predictive accuracy of BNs, as evidenced by an average AUC
exceeding 75%. The comprehensive analysis underscores the adaptability and
efficacy of Bayesian Networks in diverse clinical scenarios.
  Conclusion: The encompassing exploration of Bayesian Networks presented in
this review highlights their significance and growing impact in the realm of
disease diagnosis and prognosis. It underscores the need for further research
and development to optimize their capabilities and broaden their applicability
in addressing diverse and intricate healthcare challenges.
