AI Diet Recommendations for Adolescents Show Significant Nutritional Gaps, Study Finds

Key Takeaways:

• AI-generated diet plans consistently underestimated energy and key macronutrients required by adolescents
  
• Macronutrient balance was frequently misaligned with clinical guidelines, with lower carbohydrates and higher fat and protein levels
  
• Researchers caution that AI tools should not replace dietitians for adolescent nutrition without professional oversight
  
  
  

Growing demand for accessible nutrition support

Artificial intelligence is increasingly being used to support dietary planning, particularly in areas where access to qualified professionals is limited. However, a new study published in Frontiers in Nutrition raises important concerns about the reliability of these tools when applied to adolescents living with overweight or obesity.

Globally, adolescent overweight and obesity are rising at pace, affecting an estimated 390 million young people in 2022. In many regions, this now represents the most common form of malnutrition. Excess body weight in adolescence is associated with a range of adverse health outcomes, including type 2 diabetes, dyslipidaemia, hypertension, and sleep apnoea. It also increases the likelihood of obesity in adulthood and is linked to reduced quality of life.

Alongside physical health risks, adolescents may experience body image concerns and engage in harmful weight control behaviours such as self-induced vomiting or misuse of laxatives.

Dietary modification remains central to improving outcomes. Dietitians play a key role in delivering tailored, evidence-based nutrition plans aligned with established guidelines. However, limited access and workforce pressures can restrict the availability of personalised support.

AI tools, including chatbots and large language models, are increasingly being explored as a way to bridge this gap. While they can provide general dietary guidance, concerns remain about their accuracy, safety, and ability to replicate the individualised care provided by trained professionals.

Study design – comparing AI models with dietitian plans

To better understand the role of AI in adolescent nutrition, researchers conducted a direct comparison between AI-generated diet plans and those created by a dietitian.

Five AI systems were evaluated: ChatGPT-4o, Gemini 2.5 Pro, Claude 4.1, Bing Chat-5GPT, and Perplexity. Across two sessions, these models generated a total of 60 diet plans. Each plan covered three days and was based on four standardised adolescent profiles, including boys and girls living with overweight or obesity.

These AI-generated plans were compared with dietitian-designed one-day plans developed in line with established nutritional recommendations. The reference plans followed a macronutrient distribution of:

• 45–50 % carbohydrates
• 30–35 % fat
• 15–20 % protein

The researchers then analysed energy intake, macronutrient composition, micronutrient content, safety, and feasibility.

Consistent underestimation of energy and macronutrients

The findings revealed a clear and consistent pattern across all AI models. Diet plans generated by AI underestimated both total energy intake and key macronutrients when compared with dietitian-designed plans.

On average:

• Energy intake was lower by 695 kcal
• Protein intake was reduced by 20 g
• Fat intake was reduced by 16 g
• Carbohydrate intake was reduced by 115 g

Given the high energy demands of adolescence, such deficits could have meaningful clinical implications, particularly for growth, development, and overall health.

Macronutrient imbalance – a shift away from guidelines

Beyond total intake, the balance of macronutrients was also significantly altered in AI-generated plans.

Some AI models recommended:

• Protein intake up to 23.7 %
• Fat intake up to 44.5 %

Both values exceeded recommended levels. In contrast, carbohydrate intake accounted for no more than 36.3 %, falling below guideline recommendations.

Dietitian-designed plans, by comparison, remained closely aligned with clinical standards:

• Carbohydrates: 44 %–46 %
• Protein: 18 %–20 %
• Fat: 36 %–37 %

The authors noted:

“This pattern illustrates a systematic shift across all AI models to lower CHO, higher protein, and higher lipid meal structures, indicating that the macronutrient balance, not just the amount of gram-based nutrients, is significantly disrupted in AI-generated plans.”

Researchers suggest that AI models may be influenced by popular dietary trends, such as low-carbohydrate or ketogenic approaches, rather than evidence-based adolescent nutrition guidelines. This shift may pose risks during a critical period of physical and cognitive development.

Micronutrient variability raises additional concerns

In addition to macronutrient discrepancies, the study identified significant variability in micronutrient composition across AI-generated plans.

No model consistently matched the dietitian-designed reference diet across all nutrients. This inconsistency raises concerns about potential micronutrient deficiencies, which could further compromise adolescent health.

The findings suggest that AI tools currently lack the technical precision required to accurately estimate both macro- and micronutrient needs in personalised dietary plans for adolescents.

Strengths and limitations of the study

The study offers several notable strengths. It evaluated multiple AI models, allowing for robust comparison across systems. The use of three-day diet plans enabled identification of consistent patterns rather than isolated outputs. Dietitian-designed plans provided a credible clinical benchmark, and the inclusion of both macro- and micronutrient analysis allowed for a comprehensive assessment of dietary quality.

However, there are limitations to consider. The findings are specific to the models tested, which are rapidly evolving. Standardised adolescent profiles may not fully capture real-world complexity, limiting personalisation. The use of simulated scenarios rather than real-life behaviours may reduce ecological validity. Additionally, prompts were standardised and delivered in a single language, which may limit generalisability across populations.

Implications for clinical practice and AI use

The study highlights important risks associated with the unsupervised use of AI for adolescent dietary planning.

As the authors conclude:
“AI models have exhibited clinically significant deviations in diet plans for adolescents at both macro and micro levels.”

These deviations include consistently lower energy and carbohydrate recommendations compared with dietitian-designed plans.

Until these limitations are addressed, AI-generated diet plans should be used with caution. They may serve as a supplementary tool under professional supervision, but they are not currently a safe or reliable substitute for qualified dietary guidance in adolescents.