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.