Health Disparities through Generative AI Models: A Comparison Study
  Using A Domain Specific large language model
Yohn Jairo Parra Bautista Vinicious Lima Carlos Theran Richard Alo
Health disparities are differences in health outcomes and access to
healthcare between different groups, including racial and ethnic minorities,
low-income people, and rural residents. An artificial intelligence (AI) program
called large language models (LLMs) can understand and generate human language,
improving health communication and reducing health disparities. There are many
challenges in using LLMs in human-doctor interaction, including the need for
diverse and representative data, privacy concerns, and collaboration between
healthcare providers and technology experts. We introduce the comparative
investigation of domain-specific large language models such as SciBERT with a
multi-purpose LLMs BERT. We used cosine similarity to analyze text queries
about health disparities in exam rooms when factors such as race are used
alone. Using text queries, SciBERT fails when it doesn't differentiate between
queries text: "race" alone and "perpetuates health disparities." We believe
clinicians can use generative AI to create a draft response when communicating
asynchronously with patients. However, careful attention must be paid to ensure
they are developed and implemented ethically and equitably.