Exploratory benchmarking of AI-generated diet plans for inherited protein metabolism disorders: a simulation-based evaluation of nutritional accuracy and clinical safety
Taha Gökmen Ülger, Emre Adıgüzel · 2026
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Description
Taha Gökmen Ülger and Emre Adıgüzel test AI written diet plans for rare protein metabolism disorders in this 2026 paper. Children with these disorders must hold certain amino acids within tight bounds, so a wrong plan can harm. The team wrote standard child case scenarios for three disorders: PKU, maple syrup urine disease, and propionic acidemia. Two leading chatbots each produced three day meal plans from the same prompts. The plans were scored against reference intakes and disease specific targets.
Both models produced neat, structured plans with broadly sound nutrition. Yet both missed disease specific targets in every disorder. In the PKU case, both hit the phenylalanine range yet missed protein and tyrosine goals. Other cases split on energy or on the balance of whole and medical protein. The authors conclude that general nutrition checks cannot certify such plans.
Catalog record
| CREATOR | Taha Gökmen Ülger, Emre Adıgüzel |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 56 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |