Artificial Intelligence-Generated Diet Plans for Hypertension and Dyslipidemia: Adherence and Nutritional Insights

Basit Öğe Kaydı
dc.authorid0000-0002-0139-676X
dc.contributor.authorKenger, Emre Batuhan
dc.contributor.authorKarahan, Tugce Ozlu
dc.date.accessioned2026-04-04T18:56:04Z
dc.date.available2026-04-04T18:56:04Z
dc.date.issued2025
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractBackground: We evaluated diet plans generated by ChatGPT for hypertension and dyslipidaemia. Methods: In October 2024, ChatGPT was used to generate meal plans for 24 simulated patients with different cardiovascular health problems. Data were used from men (n=12) and women (n=12), aged 56 yr, with mean heights of 176 cm and 161 cm respectively. Weight categories were based on BMI: normal, overweight, and obese, using weights of 56, 71, and 84 kg for women and 67, 85, and 101 kg for men. Four health conditions were assessed: hypertension stages 1 and 2 (systolic BP 130-139 mm Hg and >= 140 mm Hg; diastolic BP 80-89 mm Hg and >= 90 mm Hg), and elevated LDL levels (>= 130 mg/dL and >= 160 mg/dL). Menus were evaluated for adherence to Mediterranean and DASH diets, including recommendations. Results: Adherence to the Mediterranean and DASH diets was low across all groups, with median scores below 9 and 4.5, respectively. Common recommendations included weight loss, physical activity, reduced salt intake, stress management, and omega-3s for both hypertension and LDL reduction. Plant sterols/stanols were suggested only for LDL. No advice was given on smoking or alcohol use. Nutrient content did not differ significantly between hypertension and LDL menus (P>0.05). Conclusion: This pioneering study found that AI-generated dietary models had low adherence to DASH and Mediterranean diets, though most recommendations were generally appropriate. Since the prompts only requested basic nutrition plans, future research should use more specific, personalized prompts to better assess AI's role in managing chronic diseases.
dc.identifier.doi10.18502/ijph.v54i6.18902
dc.identifier.doi10.18502/ijph.v54i6.18902
dc.identifier.endpage1251
dc.identifier.issn2251-6085
dc.identifier.issn2251-6093
dc.identifier.issue6
dc.identifier.pmid40655496
dc.identifier.scopus2-s2.0-105007709557
dc.identifier.scopusqualityQ3
dc.identifier.startpage1243
dc.identifier.urihttps://doi.org/10.18502/ijph.v54i6.18902
dc.identifier.urihttps://hdl.handle.net/11411/10664
dc.identifier.volume54
dc.identifier.wosWOS:001524093200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherIranian Scientific Society Medical Entomology
dc.relation.ispartofIranian Journal of Public Health
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectHypertension
dc.subjectDyslipidaemia
dc.subjectArtificial Intelligence
dc.subjectNutrition
dc.titleArtificial Intelligence-Generated Diet Plans for Hypertension and Dyslipidemia: Adherence and Nutritional Insights
dc.typeArticle

Dosyalar

Koleksiyon

Web of Science Indexed Publications
PubMed Indexed Publications
Scopus Indexed Publications