The article "Enhancing Health Care Communication With Large Language Models—The Role, Challenges, and Future Directions" by Charumathi Raghu Subramanian, Daniel A. Yang, and Raman Khanna explores the potential of large language models (LLMs) in improving patient communication and healthcare outcomes. LLMs, trained on extensive datasets, can generate human-like text and have shown promise in various healthcare applications, including clinical administration, note generation, and diagnostic support. Effective patient communication is crucial, and LLMs can enhance readability and understandability of written materials. The study by Zaretsky et al. demonstrates how GPT-4 can transform hospital discharge summaries into patient-readable formats. The process involved reviewing existing summaries, processing them to retain relevant patient information, and using LLMs to produce concise, readable summaries. While the LLM-generated summaries were generally shorter, more readable, and understandable, they also posed safety risks, such as omissions and hallucinations. The authors highlight the need for further development to address these safety concerns and practical challenges, such as manual processing and the need for clinician oversight. Despite these challenges, the study's early results suggest that LLMs have the potential to become important tools in enhancing healthcare communication, particularly in generating new content rather than just replacing existing ones.The article "Enhancing Health Care Communication With Large Language Models—The Role, Challenges, and Future Directions" by Charumathi Raghu Subramanian, Daniel A. Yang, and Raman Khanna explores the potential of large language models (LLMs) in improving patient communication and healthcare outcomes. LLMs, trained on extensive datasets, can generate human-like text and have shown promise in various healthcare applications, including clinical administration, note generation, and diagnostic support. Effective patient communication is crucial, and LLMs can enhance readability and understandability of written materials. The study by Zaretsky et al. demonstrates how GPT-4 can transform hospital discharge summaries into patient-readable formats. The process involved reviewing existing summaries, processing them to retain relevant patient information, and using LLMs to produce concise, readable summaries. While the LLM-generated summaries were generally shorter, more readable, and understandable, they also posed safety risks, such as omissions and hallucinations. The authors highlight the need for further development to address these safety concerns and practical challenges, such as manual processing and the need for clinician oversight. Despite these challenges, the study's early results suggest that LLMs have the potential to become important tools in enhancing healthcare communication, particularly in generating new content rather than just replacing existing ones.