Despite the impressive performance of current deep learning models in the field of medical imaging, the transfer of the lung segmentation task in X-ray images to clinical practice is still a pending task. In this study, we explored the performance of a fully automatic framework for lung fields segmentation in chest X-ray images, based on the combination of the Segment Anything Model (SAM) with prompt capabilities, and the You Only Look Once (YOLO) model to provide effective prompts. Transfer learning, loss functions and several validation strategies were intensively assessed. This provides a complete benchmark that enables future research studies to fairly compare new segmentation strategies. The results achieved demonstrate significant robustness and generalization capability against the variability in sensors, populations, disease manifestations, device processing and imaging conditions. The proposed framework is computationally efficient, can address bias in training over multiple datasets, and has the potential to be applied across other domains and modalities.