Contrary to popular belief, rare genetic diseases affect a significant portion of the global population, with a prevalence ranging from 3.5% to 8 %. These conditions are particularly prevalent among children. In Chile, the interval between the onset of symptoms and the diagnosis of these diseases can extend between six and eight years, resulting in significant emotional and economic costs for affected families. The primary reason for this delay is the dearth of knowledge about these diseases among neuropediatricians. To address this issue, we propose the implementation of a clinical decision support system (CDSS) called Diagen-AI which infers the condition or disease based on information from the child's phenotype (symptoms and signs). In addition to aiding in the diagnosis, the system offers suggestions regarding potential tests and facilitates the integration of Chilean physicians' expertise with statistical data on clinical conditions documented in Orphanet and insights from scientific literature, a novel approach for this type of solution. Diagen-AI operates by employing a Bayesian network to estimate the posteriori probability associated with the likelihood of a given condition based on observed symptoms. Testing recommendations are derived from the estimation of the impact of incorporating a new test as supplementary evidence in the prediction of conditions. The validation of Diagen-AI was conducted through the generation of synthetic data. Preliminary results were successful, for example, if the algorithm is informed with 50% of the symptoms, a correct diagnosis is achieved in 80% of the cases. With respect to the recommendation of tests, it is verified that on average 3 visits to the doctor (with 3 tests per visit) are required to achieve a correct diagnosis in 80% of the cases. We believe that Diagen-AI will be a valuable tool to shorten diagnostic periods, reducing the suffering and uncertainty of affected families by generating synthetic data.