Explore GenNAS for chest X-ray classification in lung diseases, leveraging novel parallel training methods for enhanced accuracy and efficiency. Medical image classification for pulmonary pathologies from chest X-rays is traditionally time-consuming. GenNAS, using GPT-4's generative capabilities, automates optimal architecture learning from data. This study investigates parallelization and generative algorithms to optimize neural network architectures for chest X-ray classification, analyzing their impact on the NAS algorithm using the ChexPert dataset.The study uses the CheXpert dataset with 224,316 chest X-rays, focusing on classifying five lung disease pathologies. GenNASXRays evaluates 6561 architecture possibilities in an 8-layer search space, with AUC-ROC and Precision-Recall plots as metrics. Training on 187,641 images, the sequential algorithm took 190.2 hours for an accuracy of 0.869. In parallel execution on two GPUs, an accuracy of 0.87 was achieved in 127.09 hours, highlighting the efficiency of parallelization. The experiments were executed with well-known neural network architectures for image classification such as DenseNet-121 obtaining an accuracy of 0.8678, ResNet-152 0.875 and EfficientNet-B0 0.7494 being very close to the architectures generated by GenNAS.. GenNAS demonstrates precision in defining deep learning models. Parallelization significantly accelerates Neural Architecture Search, potentially improving patient outcomes through timely and accurate diagnoses.