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Purpose: Enhanced health literacy has been linked to better health outcomes;however, few interventions have been studied. We investigate whether largelanguage models (LLMs) can serve as a medium to improve health literacy inchildren and other populations.  Methods: We ran 288 conditions using 26 different prompts throughChatGPT-3.5, Microsoft Bing, and Google Bard. Given constraints imposed by ratelimits, we tested a subset of 150 conditions through ChatGPT-4. The primaryoutcome measurements were the reading grade level (RGL) and word counts ofoutput.  Results: Across all models, output for basic prompts such as "Explain" and"What is (are)" were at, or exceeded, a 10th-grade RGL. When prompts werespecified to explain conditions from the 1st to 12th RGL, we found that LLMshad varying abilities to tailor responses based on RGL. ChatGPT-3.5 providedresponses that ranged from the 7th-grade to college freshmen RGL whileChatGPT-4 outputted responses from the 6th-grade to the college-senior RGL.Microsoft Bing provided responses from the 9th to 11th RGL while Google Bardprovided responses from the 7th to 10th RGL.  Discussion: ChatGPT-3.5 and ChatGPT-4 did better in achieving lower-gradelevel outputs. Meanwhile Bard and Bing tended to consistently produce an RGLthat is at the high school level regardless of prompt. Additionally, Bard'shesitancy in providing certain outputs indicates a cautious approach towardshealth information. LLMs demonstrate promise in enhancing health communication,but future research should verify the accuracy and effectiveness of such toolsin this context.  Implications: LLMs face challenges in crafting outputs below a sixth-gradereading level. However, their capability to modify outputs above this thresholdprovides a potential mechanism to improve health literacy and communication ina pediatric population and beyond.

ChatGPT-3.5, ChatGPT-4, Google Bard, and Microsoft Bing to Improve Health Literacy and Communication in Pediatric Populations and Beyond