One prevalent issue in health care is the presence of diagnostic discrepancy across demographics, socioeconomic levels, and genders. However, with the introduction of artificial intelligence in health care, issues such as medical misdiagnosis have the potential to identify and resolve medical concerns faster, and more efficiently for the patients’ benefit, with increasing use of AI in core infrastructure such as health care necessitating high-quality data for these models. One way in which the use of predictive modeling can be improved is by providing comprehensive and high-quality data to train these models. The researchers investigate the ability of a common model to extract meaningful and quality data related to diagnostic discrepancies. LegalAI was applied to analyze medical lawsuit documents, bridging both legal and healthcare domains. This study investigates whether AI techniques developed for legal texts can be beneficially applied in health care, particularly to identify diagnostic discrepancy patterns.