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Health disparities are differences in health outcomes and access tohealthcare between different groups, including racial and ethnic minorities,low-income people, and rural residents. An artificial intelligence (AI) programcalled large language models (LLMs) can understand and generate human language,improving health communication and reducing health disparities. There are manychallenges in using LLMs in human-doctor interaction, including the need fordiverse and representative data, privacy concerns, and collaboration betweenhealthcare providers and technology experts. We introduce the comparativeinvestigation of domain-specific large language models such as SciBERT with amulti-purpose LLMs BERT. We used cosine similarity to analyze text queriesabout health disparities in exam rooms when factors such as race are usedalone. Using text queries, SciBERT fails when it doesn't differentiate betweenqueries text: "race" alone and "perpetuates health disparities." We believeclinicians can use generative AI to create a draft response when communicatingasynchronously with patients. However, careful attention must be paid to ensurethey are developed and implemented ethically and equitably.

Health Disparities through Generative AI Models: A Comparison Study Using A Domain Specific large language model