Patient Friendly Language

Patient-friendly language research focuses on improving communication between healthcare providers and patients, primarily by leveraging artificial intelligence to process and translate complex medical information into easily understandable formats. Current research employs various machine learning models, including deep neural networks (like CNNs, LSTMs, and transformers), graph neural networks, and ensemble methods, to analyze patient data (e.g., EHRs, imaging, social media posts) and predict outcomes, personalize treatment, and enhance patient understanding of their conditions. This work holds significant implications for improving patient care, facilitating more informed decision-making, and potentially reducing healthcare disparities by addressing communication barriers and improving access to information.