{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T09:05:46Z","timestamp":1790067946371,"version":"4.0.1"},"reference-count":22,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>Background\/Objectives: Artificial intelligence (AI) tools are increasingly used for dietary assessment, but their reliability in pediatric nutrition remains uncertain. This study compared AI systems and human evaluators in pediatric menu assessment against expert-defined reference ratings. Methods: This observational cross-sectional study used anonymized dietary and clinical information from three healthy pediatric cases. A multidisciplinary panel of pediatric nutrition specialists established standard evaluations. Assessments were completed by 84 AI evaluations, 116 nutrition specialists, 56 pediatric-focused physicians, and 88 physicians from other specialties. Outcomes included absolute error in total energy estimation, deviations in portion and qualitative ratings, standardized scores, and binary adequacy accuracy. Groups were compared using ANOVA or Kruskal\u2013Wallis tests with post hoc analyses. Results: A total of 344 assessments were included. Energy-estimation error differed between groups (Kruskal\u2013Wallis = 12.85, p = 0.0016), but this analysis was based on a limited and highly unbalanced subset of 77 assessments. AI had the largest mean absolute error (260.3 \u00b1 194.3 kcal), followed by nutrition specialists (148.4 \u00b1 81.5 kcal); physicians with other specialties had the lowest error (60.4 \u00b1 53.3 kcal); these subgroup comparisons should be interpreted cautiously because of the small and unequal numbers of available energy estimates. AI showed greater portion-rating deviation (0.488; 95% CI: 0.38\u20130.60) than nutrition specialists (0.310; 95% CI: 0.20\u20130.42; p = 0.0019, q = 0.0059) and pediatric-focused physicians (0.286; 95% CI: 0.16\u20130.41; p = 0.0181, q = 0.019). Conversely, AI showed the lowest deviations for variety (0.37 \u00b1 0.49) and processing (0.33 \u00b1 0.47), compared with 1.15\u20131.36 and 1.22\u20131.57, respectively, among human groups (both p = 0.0001). Conclusions: AI may support structured pediatric menu screening for descriptive qualitative features; however, lower precision for energy and portion assessment supports, under these specific conditions, its use as an adjunct, for qualified nutrition professionals.<\/jats:p>","DOI":"10.3390\/nu18183106","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T16:06:51Z","timestamp":1790006811000},"page":"3106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Domain-Dependent Performance of Human Experts and AI Systems in Pediatric Menu Evaluation"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1499-5398","authenticated-orcid":false,"given":"Roxana Maria","family":"Martin-Hadma\u0219","sequence":"first","affiliation":[{"name":"Department of Community Nutrition and Food Safety, \u201cGeorge Emil Palade\u201d University of Medicine, Pharmacy, Science and Technology of T\u00e2rgu Mures, Gheorghe Marinescu 38, 540139 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"}]},{"given":"Rebeca","family":"Sovea","sequence":"additional","affiliation":[{"name":"Department of Community Nutrition and Food Safety, \u201cGeorge Emil Palade\u201d University of Medicine, Pharmacy, Science and Technology of T\u00e2rgu Mures, Gheorghe Marinescu 38, 540139 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"}]},{"given":"Diana","family":"Pol","sequence":"additional","affiliation":[{"name":"Department of Community Nutrition and Food Safety, \u201cGeorge Emil Palade\u201d University of Medicine, Pharmacy, Science and Technology of T\u00e2rgu Mures, Gheorghe Marinescu 38, 540139 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9594-1231","authenticated-orcid":false,"given":"George Mih\u0103i\u021b\u0103","family":"Gavra","sequence":"additional","affiliation":[{"name":"Center for Advanced Medical and Pharmaceutical Research, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7299-118X","authenticated-orcid":false,"given":"Monica","family":"Tarcea","sequence":"additional","affiliation":[{"name":"Department of Community Nutrition and Food Safety, \u201cGeorge Emil Palade\u201d University of Medicine, Pharmacy, Science and Technology of T\u00e2rgu Mures, Gheorghe Marinescu 38, 540139 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"Adriana","family":"Neghirl\u0103","sequence":"additional","affiliation":[{"name":"Mures School Medicine Association, 540233 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"\u0218tefan Adrian","family":"Martin","sequence":"additional","affiliation":[{"name":"Center for Advanced Medical and Pharmaceutical Research, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e44030","DOI":"10.2196\/44030","article-title":"Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges","volume":"25","author":"Fabelo","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.1007\/s12070-025-05451-2","article-title":"Parental Perception on Usage of AI Chatbot to Understand Paediatric Otorhinolaryngology Condition: A Survey","volume":"77","author":"Zakirah","year":"2025","journal-title":"Indian J. Otolaryngol. Head Neck Surg."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huang, Y.-D., Zeng, S.-L., Lin, J., Mao, Z.-P., Zhang, Q.-L., Feng, G.-Z., and Ou, Q.-X. (2025). Parents\u2019 Understanding and Attitudes toward the Application of AI in Pediatric Healthcare: A Cross-Sectional Survey Study. Front. Public Health, 13.","DOI":"10.3389\/fpubh.2025.1654482"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"O\u2019Hara, C., Kent, G., Flynn, A., Gibney, E.R., and Timon, C.M. (2025). An Evaluation of ChatGPT for Nutrient Content Estimation from Meal Photographs. Nutrients, 17.","DOI":"10.3390\/nu17040607"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1001\/jamapediatrics.2024.0314","article-title":"Artificial Intelligence Chatbots and Their Influence on Learning","volume":"178","author":"Movaghar","year":"2024","journal-title":"JAMA Pediatr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e51432","DOI":"10.2196\/51432","article-title":"Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review","volume":"26","author":"Chotwanvirat","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Phalle, A., and Gokhale, D. (2025). Navigating Next-Gen Nutrition Care Using Artificial Intelligence-Assisted Dietary Assessment Tools-a Scoping Review of Potential Applications. Front. Nutr., 12.","DOI":"10.3389\/fnut.2025.1518466"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, X., Yin, A., Choi, H.Y., Chan, V., Allman-Farinelli, M., and Chen, J. (2024). Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care. Nutrients, 16.","DOI":"10.3390\/nu16152573"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Belkhouribchia, J., and Pen, J.J. (2025). Large language models in clinical nutrition: An overview of its applications, capabilities, limitations, and potential future prospects. Front. Nutr., 12.","DOI":"10.3389\/fnut.2025.1635682"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ase, A., Borowicz, J., Rakocy, K., and Piekarska, B. (2025). Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert Oversight. Nutrients, 18.","DOI":"10.3390\/nu18010023"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e43","DOI":"10.1017\/jns.2023.27","article-title":"Technological Tools for Assessing Children\u2019s Food Intake: A Scoping Review","volume":"12","author":"Freitas","year":"2023","journal-title":"J. Nutr. Sci."},{"key":"ref_12","unstructured":"(2026, August 17). Food Search|USDA FoodData Central, Available online: https:\/\/fdc.nal.usda.gov\/food-search?type=Branded&query=bread."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sosa-Holwerda, A., Park, O.-H., Albracht-Schulte, K., Niraula, S., Thompson, L., and Oldewage-Theron, W. (2024). The Role of Artificial Intelligence in Nutrition Research: A Scoping Review. Nutrients, 16.","DOI":"10.3390\/nu16132066"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e54557","DOI":"10.2196\/54557","article-title":"Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review","volume":"26","author":"Zheng","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bilen, A.B., Kalkan, G.E., and \u00d6nal, H.Y. (2026). Artificial Intelligence Diet Plans Underestimate Nutrient Intake Compared to Dietitians in Adolescents. Front. Nutr., 13.","DOI":"10.3389\/fnut.2026.1765598"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e70135","DOI":"10.1111\/jhn.70135","article-title":"Artificial Intelligence in Clinical Nutrition: A Descriptive Comparison of ChatGPT- and Dietitian-Planned Diets for Chronic Disease Scenarios","volume":"38","author":"Onay","year":"2025","journal-title":"J. Hum. Nutr. Diet. Off. J. Br. Diet. Assoc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"17","DOI":"10.21037\/mhealth-2025-70","article-title":"Evaluation of AI-Generated versus Registered Dietitian-Authored Nutrition Responses: A Cross-Sectional Study","volume":"12","author":"Ayres","year":"2026","journal-title":"mHealth"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liao, L.-L., Chang, L.-C., and Lai, I.-J. (2024). Assessing the Quality of ChatGPT\u2019s Dietary Advice for College Students from Dietitians\u2019 Perspectives. Nutrients, 16.","DOI":"10.3390\/nu16121939"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kassem, H., Beevi, A.A., Basheer, S., Lutfi, G., Cheikh Ismail, L., and Papandreou, D. (2025). Investigation and Assessment of AI\u2019s Role in Nutrition-An Updated Narrative Review of the Evidence. Nutrients, 17.","DOI":"10.3390\/nu17010190"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2138672","DOI":"10.1080\/19490976.2022.2138672","article-title":"Artificial Intelligence-Based Personalized Diet: A Pilot Clinical Study for Irritable Bowel Syndrome","volume":"14","author":"Karakan","year":"2022","journal-title":"Gut Microbes"},{"key":"ref_21","first-page":"148","article-title":"Artificial Intelligence For Personalized Nutrition: Mobile Dietary Assessment, Childhood Nutritional Classification, And Individualized Food Recommendations","volume":"4","author":"Habib","year":"2026","journal-title":"Front. Med. Health Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Panayotova, G.G. (2025). Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research. Healthcare, 13.","DOI":"10.3390\/healthcare13202579"}],"container-title":["Nutrients"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6643\/18\/18\/3106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T08:32:28Z","timestamp":1790065948000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6643\/18\/18\/3106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,21]]},"references-count":22,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["nu18183106"],"URL":"https:\/\/doi.org\/10.3390\/nu18183106","relation":{},"ISSN":["2072-6643"],"issn-type":[{"value":"2072-6643","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,21]]}}}