The role of artificial intelligence in developing meal plans in outpatient dietetics: A feasibility and proof of concept study.

Clinician–Artificial Intelligence Collaboration for Mediterranean Meal-Plan Generation: Development, Technical Feasibility, and Professional Acceptability of the MAI-DIET Framework

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.

Assessing Hospital Patient Nutrient Intake with an AI-Powered Food Recognition System – A Feasibility Study of the FlavoriaFlex solution

Adequate dietary intake is essential for positive clinical outcomes of hospitalized patients, yet monitoring food intake is labor-intensive and often subjective. AI-based food recognition could automate monitoring and assessment, but evidence in real-world hospital settings is limited. This study evaluated an AI-powered food recognition system, FlavoriaFlex, to assess its detection performance, deployment feasibility, and acceptability among dietitians. Previously validated in restaurant (F1 0.75, weight MAE 23.6 g, energy MAE 235 kcal), the system was deployed in a hospital ward for six days. A total of 133 meals were recorded; 102 had paired leftover images (235 total images). Manual annotation of 483 food segments provided ground truth for evaluating food recognition and menu mapping. Semi-structured interviews with dietitians assessed usability, perceived benefits, and clinical value. FlavoriaFlex enabled automatic estimation of item- and meal-level consumption, including weights and energy- and macronutrient contents. Overall food recognition accuracy was 94% (F1 0.76), remaining high for served meals (96.5%, F1 0.85) and robust for visually complex leftovers (89.5%, F1 0.71). Unknown/non-food segments were minimal (2.4% of leftovers; 0.27% of weight). A web dashboard delivered real-time visualizations, including energy and nutrient intake. Dietitians reported reduced cognitive burden, more objective assessment, and improved observability into patient dietary intake, while emphasizing the need for further validation and integration for clinical use. These findings demonstrate that FlavoriaFlex could be integrated into hospital workflows to provide accurate, clinically meaningful intake estimates, with AI-assisted food recognition offering an efficient, reliable approach to improving nutritional monitoring at scale.

On the road to fully automated insulin delivery: A systematic review of meal announcement free algorithms

Fully automated insulin delivery (FAID) systems aim to regulate blood glucose levels in individuals with type 1 diabetes with minimal user input. A key challenge to achieving full automation is the need for manual meal announcements. This review focuses on algorithms developed to detect and compensate for unannounced meals. A systematic literature search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science (2000 – 2025), and the review was reported in accordance with PRISMA guidelines. From 1205 initially retrieved articles, 69 studies met predefined eligibility criteria and were included in the final review. Extracted data included algorithm type, data source (in-silico or clinical), performance metrics, and glycemic outcomes where available. The reviewed algorithms include heuristic rule-based methods, machine learning models, and control systems theory approaches. While varying in complexity and detection strategies, these approaches aim to identify meal events and estimate carbohydrate intake for timely insulin dosing. Median performance across studies included a sensitivity of 88%, precision of 93%, and detection times ranging from 25 to 40 minutes. In-silico evaluations generally reported more false positives than in-vivo. Reported glycemic outcomes demonstrated time-in-range values between 65% and 89%, highlighting the potential of these systems to support fully autonomous glucose regulation. Automated meal detection and compensation algorithms show promise for integration into FAID systems, with encouraging detection performance and glycemic outcomes. However, the evidence remains heterogeneous, and a substantial portion is still based on in-silico studies rather than clinical validation. Challenges remain in minimizing false positives, ensuring generalizability across populations, and validating performance in real-word settings.

Dietitians' Views on Artificial Intelligence: An In-Depth Study

Aim: Today, the use of artificial intelligence technologies is becoming increasingly common in many areas from health services to nutrition planning. The aim of this study is to explore dietitians' perceptions, experiences, and professional perspectives on the role of artificial intelligence in the field of nutrition.Method: In this qualitative study, online in-depth interviews were conducted with 29 dietitians working in different sectors between March 27 and April 28, 2025. In the interviews lasting 15-20 minutes, a semi-structured topic guide consisting of 10 questions was used. Transcripts were analyzed using the thematic analysis method.Results: Dietitians' statements were evaluated under the themes of (1) professional transformation and the role of artificial intelligence, (2) ethics, trust and professional responsibility, (3) professional competence, education and adaptation, (4) Scientific validity, reliability and personalization. Dietitians evaluated artificial intelligence as a helpful tool that saves time and facilitates routine tasks, but emphasized that human interaction, empathetic communication, and ethical responsibility cannot be replaced by technology. Although it is thought that artificial intelligence will lead to a radical change in the field of nutrition, there are concerns about issues such as patient privacy, ethical values, and lack of scientific evidence.Conclusion: To our knowledge, this is the first study in Türkiye to explore dietitians’ perspectives on artificial intelligence in nutrition. This study shows that artificial intelligence presents both risks and opportunities in the dietitian profession, and draws attention to the fact that professionals should be equipped in terms of knowledge, ethics and digital competence in this transformation process.

Customized Diet Plan Generator Using Python and Machine Learning Tools

Reimagining Home Enteral Nutrition Through Artificial Intelligence: A Narrative Review of Clinical, Operational, and Patient-Centered Applications

Home enteral nutrition (HEN) is essential for patients with a functioning gastrointestinal tract who cannot meet nutritional needs orally, yet outpatient management remains complex, resource-intensive, and supported by a limited evidence base. Artificial intelligence (AI) may augment HEN care by extending monitoring, education, risk assessment, documentation support, and operational coordination into the home environment. Consistent with the narrative review format, this article uses a pragmatic, transparent synthesis of influential HEN-specific literature, relevant clinical nutrition evidence, and background knowledge from adjacent fields, including home healthcare, chronic disease management, oncology nutrition, telehealth, and software regulation. Direct evidence in established HEN populations remains scarce; therefore, most AI applications should be considered hypotheses or early implementation opportunities rather than proven standards of care. The strongest near-term opportunities are clinician-supervised patient education, symptom triage, adherence support, remote monitoring, and workflow automation. Predictive analytics, smart pumps, and precision enteral prescription tools are promising but require prospective HEN-specific validation, interoperability with electronic health records and home-infusion systems, reimbursement pathways, and governance safeguards. Key barriers include dataset bias, limited external validation, alert fatigue, privacy and regulatory concerns, unclear accountability, digital equity, cost uncertainty, and the risk of dehumanizing care. AI should be viewed as a complement to multidisciplinary HEN expertise. Priorities for the near future include HEN registries, standardized outcomes, prospective validation, pragmatic implementation trials, health-economic evaluation, and transparent oversight that preserves clinician accountability and patient-centered care.