This chapter provides a comprehensive review of deep learning techniques employed for generating accurate and reliable radiology reports from chest X-ray images. The primary focus of this review is to highlight the advancements and potential applications of deep learning models in enhancing medical diagnosis prediction. A significant contribution of this review is the identification of a Bidirectional GRU model with attention as a superior performer compared to a basic model. This advanced model demonstrates remarkable proficiency in accurately predicting both short and long sentences, surpassing the baseline model’s accuracy. Future research can concentrate on developing techniques capable of handling larger and more intricate datasets while simultaneously enhancing accuracy and reliability. Personalized medicine emerges as a promising area for exploration, leveraging patient-specific data to tailor treatment plans according to individual needs. The ethical considerations associated with the development and application of medical diagnosis prediction algorithms are emphasized. Future research endeavors should address concerns such as patient privacy and data analysis bias. Additionally, exploring the integration of prediction algorithms with telemedicine and remote monitoring technologies holds potential for enhancing patient care and outcomes.