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Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5\u2010year period were obtained and reinterpreted utilizing the previously validated Franklin\u00a9 Artificial Intelligence (AI). Using PowerBI\u00a9, the data were further matched to patients in the electronic healthcare record to associate with demographic data to generate a variant data table and mapped by ZIP codes. Three thousand and sixty\u2010five variants were identified and 98% were matched to patients with geographic data. Franklin\u00a9 changed the interpretation for 24% of variants. One hundred and fifty\u2010six clinically actionable variant reinterpretations were made. A total of 739 Mendelian genetic disorders were identified with disorder prevalence estimation. Mapping of variants demonstrated hot\u2010spots for pathogenic genetic variation such as <jats:italic>PEX6<\/jats:italic>\u2010associated Zellweger Spectrum Disorder. Seven patients were identified with Bardet\u2010Biedl syndrome and seven patients with Rett syndrome amenable to newly FDA\u2010approved therapeutics. Utilizing readily available software we developed a database and Exploratory Data Analysis (EDA) methodology enabling us to systematically reinterpret variants, estimate variant prevalence, identify conditions amenable to new treatments, and localize geographies enriched for pathogenic variants.<\/jats:p>","DOI":"10.1002\/ajmg.a.63505","type":"journal-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T07:14:27Z","timestamp":1704266067000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data"],"prefix":"10.1002","volume":"194","author":[{"given":"Suellen","family":"Jackson","sequence":"first","affiliation":[{"name":"Valley Children's Hospital  Madera California 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