{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Basic Health Sciences, College of Applied Medical Sciences, Qassim University, P.O. Box 6666, Buraydah 51452, Saudi Arabia",
    "Clinical Nutrition Department, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia",
    "Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia",
    "Department of Clinical Nutrition, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia",
    "Department of Biotechnology and Microbiology, Khandelwal College of Management, Science and Technology, M.J.P. Rohilkhand University, Bareilly 243006, India"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mohammad Idreesh Khan",
      "orcid": "0000-0001-9325-6491"
    },
    {
      "affs": [
        1
      ],
      "name": "Abrar Turki",
      "orcid": "0000-0002-3956-1150"
    },
    {
      "affs": [
        2
      ],
      "name": "Shadaid Alanezi",
      "orcid": "0009-0002-6667-5150"
    },
    {
      "affs": [
        1
      ],
      "name": "Malak Ghazi Almutairi",
      "orcid": "0000-0001-7759-6437"
    },
    {
      "affs": [
        3
      ],
      "name": "Fauzia Ashfaq",
      "orcid": "0000-0003-3653-4381"
    },
    {
      "affs": [
        4
      ],
      "name": "Mohd. Faheem Khan",
      "orcid": "0000-0002-8911-3268"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/370kzmpxsn809.bib",
  "cited_by": 0,
  "doi": "10.3390/nu18203406",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13708203/?tool=EBI",
  "id": "370kzmpxsn809",
  "issns": [
    "2072-6643"
  ],
  "landing_url": "https://www.mdpi.com/2072-6643/18/20/3406",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2739|Nutritional Studies and Diet"
    ]
  ],
  "openalex": "W7222920022",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13708203/pdf/?tool=EBI",
  "pmcid": "PMC13708203",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/370kzmpxsn809.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:56Z",
      "native_id": "10.3390/nu18203406",
      "source": "crossref",
      "url": "https://www.mdpi.com/2072-6643/18/20/3406"
    },
    {
      "first_seen": "2026-10-30T12:21:45Z",
      "native_id": "PMC:PMC13708203",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13708203"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2739|Nutritional Studies and Diet",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Artificial Intelligence-Driven Dietary Assessment Software: A Structured Narrative Review of Technological Advances, Persistent Measurement Error and Barriers to Clinical Translation",
  "title_text": "Artificial Intelligence-Driven Dietary Assessment Software: A Structured Narrative Review of Technological Advances, Persistent Measurement Error and Barriers to Clinical Translation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:21:45Z",
  "url": "https://research.draconitic.com/paper/370kzmpxsn809",
  "venue": "Nutrients",
  "versions": []
}