Generating medical reports from medical images using traditional methods is a time-consuming process that is prone to human error and requires experience. Failure to generate fast reports from medical images delays the treatment of patients, and misdiagnosis can lead to adverse conditions that can cause the death of patients. The main objective of this study is to develop a high-performance deep learning model that can autonomously generate medical reports from medical images. The proposed model consists of a Vision Transformer (ViT) encoder and a Bidirectional Autoregressive Transformer (BART) decoder. Training and testing on the model was conducted using images and reports from the Indiana University Chest X-Ray dataset. The developed model is analyzed with measurable parameters and then compared with its competitors in the literature using the same dataset. The proposed Vi-Ba architecture achieved success scores of 0.150, 0.154, 0.274 in bleu-4, meteor and rouge word matching evaluation metrics, respectively. The Vi-Ba model achieved high reporting performance compared to the studies reviewed in the literature. The results show that the proposed architecture can be used by specialized doctors in hospitals to diagnose diseases faster and more accurately. In this way, misdiagnosis and treatments will be reduced and human life will be protected.