Large language models will soon become a much bigger part of doctors’ clinical workflows, former FDA Commissioner Scott Gottlieb said Tuesday at the 3rd Annual Summit on the Future of Rural Health Care.
It’s important that we focus attention on the areas where AI is already having a significant impact. These are the practical applications that will allow the best and brightest researchers to focus on science rather than data munging.
The progress in artificial intelligence (AI) is reshaping patient care, across various dimensions, from facilitating faster discharge to curating treatment plans and suggesting lifestyle changes.
Researchers at the University of Massachusetts Amherst released a paper this week showing that large language models tend to hallucinate quite a bit when producing medical summaries.
OpenAI’s GPT-4 was able to identify suicidal ideation with similar accuracy to clinicians, but in a much shorter amount of time, a new study from Brightside Health found.
Anthropic's Claude3-Opus performed better than GPT-4, but both fell short of humans on a test of objective medical knowledge. The study was conducted by a firm developing LLMs specifically for healthcare that claim to be incorporating peer-reviewed sources of information.
LLMs bring great potential to help the healthcare industry center care around patients’ needs by improving communication, access, and engagement. However, LLMs also present significant challenges associated with privacy and bias that also must be considered.
The future looks bright for the integrated specialty pharmacy model – for health systems, providers, and most importantly, for patients.
As the healthcare industry grapples with various challenges, from workforce shortages to complex coding intricacies, embracing AI can empower revenue cycle professionals to envision a future with reduced denials, optimized revenue, and fortified financial health.
This month, the World Health Organization released new guidelines on the ethics and governance of LLMs in healthcare. Reactions from the leaders of healthcare AI companies have been mainly positive. However, one leader pointed out that fear or the risks associated with LLMs shouldn't hinder innovation, and another noted that the guidance may have failed to mention a major topic.
A recent academic study of the leading LLM AI models’ ability to read and interpret clinical information demonstrates the incredible power of these tools to support healthcare, but concludes that LLMs should be used as “a supplement to existing workflows rather than as a replacement for human expertise."