Large language models have proliferated across multiple domains in as short
period of time. There is however hesitation in the medical and healthcare
domain towards their adoption because of issues like factuality, coherence, and
hallucinations. Give the high stakes nature of healthcare, many researchers
have even cautioned against its usage until these issues are resolved. The key
to the implementation and deployment of LLMs in healthcare is to make these
models trustworthy, transparent (as much possible) and explainable. In this
paper we describe the key elements in creating reliable, trustworthy, and
unbiased models as a necessary condition for their adoption in healthcare.
Specifically we focus on the quantification, validation, and mitigation of
hallucinations in the context in healthcare. Lastly, we discuss how the future
of LLMs in healthcare may look like.

在医疗保健领域，大型语言模型的采用存在对可信度、连贯性和幻觉等问题的犹豫。本文讨论了在医疗保健领域中实现可靠、可信赖和无偏倚模型的关键因素，并重点关注在医疗保健背景下幻觉的量化、验证和缓解。最后，我们讨论了大型语言模型在医疗保健中的未来。