Summary
The barrage of misinformation in the field of healthcare is persistent and growing. The advent of artificial intelligence (AI) and large language models (LLMs) in healthcare has expedited the increase in misinformation, and LLMs are susceptible to false output if they are trained on incorrect healthcare information. This risk of misinformation is especially true for LLMs trained on vast datasets of information originating from online sources and can be particularly difficult to navigate when developers do not disclose the databases used to train such tools. Incorrect medical advice generated from LLMs have serious consequences for patients. How can we quantify and ultimately reduce the misinformation caused by LLMs to ensure better patient health outcomes?
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