Note Generation

Note generation research focuses on automatically creating clinical documentation from doctor-patient conversations, aiming to reduce physician workload and improve patient care. Current efforts leverage large language models (LLMs), often incorporating techniques like retrieval-augmented generation and parameter-efficient fine-tuning, to generate structured notes (e.g., SOAP notes) from audio or text inputs. This field is actively developing improved evaluation metrics, addressing challenges in ensuring accuracy, completeness, and consistency, and exploring methods for personalized note generation and efficient model deployment in resource-constrained settings. The ultimate goal is to create reliable and efficient AI-assisted tools that enhance clinical workflows and reduce administrative burden on healthcare professionals.