Artificial Intelligence-Driven Dietary Assessment Software: A Structured Narrative Review of Technological Advances, Persistent Measurement Error and Barriers to Clinical Translation
5 affiliations
- 1Department of Basic Health Sciences, College of Applied Medical Sciences, Qassim University, P.O. Box 6666, Buraydah 51452, Saudi Arabia
- 2Clinical Nutrition Department, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia
- 3Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia
- 4Department of Clinical Nutrition, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia
- 5Department of Biotechnology and Microbiology, Khandelwal College of Management, Science and Technology, M.J.P. Rohilkhand University, Bareilly 243006, India
Abstract
Background: Accurate dietary-intake assessment underpins nutrition research and clinical dietetics, yet self-report methods remain vulnerable to recall, portion-size and reporting error. Objective: This structured narrative review evaluates AI-enabled and sensor-supported dietary-assessment software, focusing on technical performance, measurement validity, user engagement, food-composition databases, cultural applicability and clinical readiness. Methods: Evidence was identified through searches of five electronic sources—PubMed (including MEDLINE-indexed records), Scopus, Web of Science Core Collection, IEEE Xplore and ScienceDirect—covering January 2020 to March 2025, and a complete rerun of the same five sources on 7 August 2026, supplemented by backward and forward citation tracking. The retained primary screening record comprised 1620 electronic-source records and 42 additional records (1662 before deduplication); 478 duplicates were removed, 1184 records were screened, 212 full texts were assessed and 15 sources were retained for the primary-phase qualitative synthesis. These figures are retained aggregate review-log totals and should not be interpreted as a fully reconstructable systematic-review audit trail. The 2026 update incorporated 18 publications: 10 from the January 2020–March 2025 historical interval through separate recovery/reassessment, and eight published after March 2025 through the 497-record update-specific screening stream, with retrieval phase reported separately from evidence role. The higher, moderate and limited/emerging within-map convergence descriptors summarise patterns in the purposively selected literature and do not represent exhaustive retrieval, formal certainty of evidence or reproducible screening. Results: Food recognition is most mature for visually distinct foods under controlled conditions. Portion-size estimation, nutrient quantification, mixed dishes, sustained engagement and culturally diverse foods remain less consistent. Selected systems show reasonable group-level energy and macronutrient agreement under defined conditions, whereas validation in free-living and clinical settings remains variable; a 2026 doubly labelled water study reported substantial underestimation and poor individual agreement in females with obesity. Conclusions: Current tools may support self-monitoring, research screening and dietitian-assisted assessment, but stand-alone use for high-stakes therapeutic decisions requires independent, nutrient-specific validation in the target population.
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BibTeX
@article{khan2026artificial,
title = {{Artificial Intelligence-Driven Dietary Assessment Software: A Structured Narrative Review of Technological Advances, Persistent Measurement Error and Barriers to Clinical Translation}},
author = {Mohammad Idreesh Khan and Abrar Turki and Shadaid Alanezi and Malak Ghazi Almutairi and Fauzia Ashfaq and Mohd. Faheem Khan},
journal = {Nutrients},
year = {2026},
month = oct,
doi = {10.3390/nu18203406},
url = {https://www.mdpi.com/2072-6643/18/20/3406},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence-Driven Dietary Assessment Software: A Structured Narrative Review of Technological Advances, Persistent Measurement Error and Barriers to Clinical Translation
AU - Mohammad Idreesh Khan
AU - Abrar Turki
AU - Shadaid Alanezi
AU - Malak Ghazi Almutairi
AU - Fauzia Ashfaq
AU - Mohd. Faheem Khan
PY - 2026
DA - 2026/10/01
JO - Nutrients
DO - 10.3390/nu18203406
UR - https://www.mdpi.com/2072-6643/18/20/3406
AB - Background: Accurate dietary-intake assessment underpins nutrition research and clinical dietetics, yet self-report methods remain vulnerable to recall, portion-size and reporting error. Objective: This structured narrative review evaluates AI-enabled and sensor-supported dietary-assessment software, focusing on technical performance, measurement validity, user engagement, food-composition databases, cultural applicability and clinical readiness. Methods: Evidence was identified through searches of five electronic sources—PubMed (including MEDLINE-indexed records), Scopus, Web of Science Core Collection, IEEE Xplore and ScienceDirect—covering January 2020 to March 2025, and a complete rerun of the same five sources on 7 August 2026, supplemented by backward and forward citation tracking. The retained primary screening record comprised 1620 electronic-source records and 42 additional records (1662 before deduplication); 478 duplicates were removed, 1184 records were screened, 212 full texts were assessed and 15 sources were retained for the primary-phase qualitative synthesis. These figures are retained aggregate review-log totals and should not be interpreted as a fully reconstructable systematic-review audit trail. The 2026 update incorporated 18 publications: 10 from the January 2020–March 2025 historical interval through separate recovery/reassessment, and eight published after March 2025 through the 497-record update-specific screening stream, with retrieval phase reported separately from evidence role. The higher, moderate and limited/emerging within-map convergence descriptors summarise patterns in the purposively selected literature and do not represent exhaustive retrieval, formal certainty of evidence or reproducible screening. Results: Food recognition is most mature for visually distinct foods under controlled conditions. Portion-size estimation, nutrient quantification, mixed dishes, sustained engagement and culturally diverse foods remain less consistent. Selected systems show reasonable group-level energy and macronutrient agreement under defined conditions, whereas validation in free-living and clinical settings remains variable; a 2026 doubly labelled water study reported substantial underestimation and poor individual agreement in females with obesity. Conclusions: Current tools may support self-monitoring, research screening and dietitian-assisted assessment, but stand-alone use for high-stakes therapeutic decisions requires independent, nutrient-specific validation in the target population.
SN - 2072-6643
ER -