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Clinician-Artificial Intelligence Collaboration for Mediterranean Meal-Plan Generation: Development, Technical
Konstantinos Divanis1, Alexandra Foscolou1, Georgios Prosalentis1
1Department of Nutritional Science and Dietetics, School of Health Sciences, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece.
Background/Objectives:
Adherence to the Mediterranean diet has declined in recent decades, highlighting the need for practical, technology-enabled tools to support its adoption. This study developed and evaluated MAI-DIET, a clinician-supervised, data-driven, AI-assisted programmatic framework designed to generate Greek-Mediterranean recipe-based meal plans for apparently healthy community-dwelling adults.
Methods:
MAI-DIET is combined with standardized food-composition and recipe libraries and a rule-based module that performs meal-plan generation and nutrient calculations. Claude Opus 4.7 supported predefined operator-supervised transformation tasks, and provided the interface for launching the rule-based module. Six 28-day recipe-based dietary plans were generated for hypothetical adults with energy goals ranging from 1600 to 2600 kcal/day. The generated plans were not tested in the intended population. For each plan, detailed nutritional analysis was performed according to predefined criteria. The quality of the dietary plans was assessed using validated scores, i.e., MedDietScore, dietary phytochemical index (DPI), and GR-UPFAST. Early-stage acceptability was evaluated by 103 healthcare professionals using a five-point Likert questionnaire.
Results:
All plans met the predefined energy criteria, while most nutrient targets were achieved. Deviations were observed for sodium, particularly in the higher-energy plans (reaching +36.3%), and for calcium, which was 17.5% below the EFSA population reference intake in the 1600 kcal/day plan. MedDietScore ranged between 35 and 36/55, while DPI was 47.0-50.9% and GR-UPFAST was 2.0-4.5/70. Overall acceptability was favorable (3.84 ± 0.59), with Cronbach's alpha values of 0.838-0.952.
Conclusions:
The findings support the technical feasibility of the MAI-DIET framework and its preliminary acceptability among healthcare professionals. Further evaluation is required before conclusions can be drawn regarding its practical effectiveness.
