AI can Outperform Humans in Writing Medical Summaries

Physicians meet with dozens of patients every day and make critical health-related recommendations based on notes, patient descriptions, test results, and diagnostic information gathered in those meetings. All this textual information is typically amassed in the patient’s electronic health record (EHR). The sheer volume of information in electronic health records has become an inflection point in modern medicine. Most doctors now rely on short summaries of long-form notes and medical records to manage patient care. “The clinical burden of medical documentation is high, and it is time-consuming work. And this has consequences for patients,” says Dave Van Veen, a doctoral candidate in electrical engineering and first author of a new study in the journal Nature Medicine, who is exploring the possibilities of AI-assisted summarization. “Doctors have less time for patient care and there is always the possibility of error anytime you are summarizing information from EHR.” In the study, Van Veen and colleagues at Stanford University have adapted eight large language models (LLMs) to clinical text and tested their summarization skills against those of human medical experts. More often than not, the researchers say, physicians preferred summaries generated by AI to those done by humans.