The impact that large language models have had both on the medical community and IT developers is undeniable. But are they being trained with the relevant data and the right kind of self-supervision? These are questions that need to be asked, according to Mayo Clinic Platform researchers.
When leveraged for their original purpose – to “create a skeleton note for humans to augment and edit, reducing administrative burden – generative AI can help provide more time for patient care and clinical decision making, according to John Halamka, MD, and Paul Cerrato of Mayo Clinic Platform.
Large language models offer great potential for improving care and efficiency, but “that must be done within guardrails and guidelines so that we do no digital harm,” said Paul Cerrato and John Halamka, MD. In this piece, they discuss the key components for successful development and deployment of AI tools.
“We are so hell-bent on simplifying our work, reducing our EHR burden, we sometimes forget that this work is more than just pointing, clicking, and typing.” In this piece, CT Lin, MD, CMIO at UCHealth, examines the downside of inserting GPT assistants into the EHR and suggests a different strategy for leveraging digital tools.
Large language models and GPT may not be the apocalyptic nightmare some critics fear, but they certainly require due diligence by thought leaders, health care professionals, and the general public, wrote John Halamka, MD, and Paul Serrato. “We need a framework to manage them responsively.”