Nutrients
Artificial Intelligence-Driven Dietary Assessment Software: A Structured Narrative Review of Technological Advances, Persistent Measurement Error and Barriers to Clinical Translation
By Mohammad Idreesh Khan, Abrar Turki, Shadaid Alanezi and 3 others.
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.