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Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools—a scoping review of potential applications
Anuja Phalle, Devaki Gokhale. Frontiers in Nutrition, 2025-01-23
Topic: Risk, prevention and care in population studies
We observed that existing AI-assisted dietary assessment tools are integrated with mobile/web-based applications to provide a user-friendly interface. These tools can broadly be categorized as "Image-based" and "Motion sensor-based." Image-based tools allow food recognition, classification, food volume/weight, and nutrient estimation whereas, Motion sensor-based tools help capture eating occasions through wrist movement, eating sounds, jaw motion & swallowing. These functionalities capture the dietary data regarding the type of food or beverage consumed, calorie intake, portion sizes, frequency of eating, and shared eating occasions as real-time data making it more accurate as against conventional dietary assessment methods. Dietary assessment tools integrated with AI and ML could estimate real-time energy and macronutrient intake in patients with chronic conditions such as obesity, diabetes, and dementia. Additionally, these tools are non-laborious, time-efficient, user-friendly, and provide fairly accurate data free from recall/reporting bias enabling clinicians to offer personalized nutrition. Therefore, integrating AI-based dietary assessment tools will help improve the quality of nutrition care and navigate next-gen nutrition care practices. More studies are required further to evaluate the efficacy and accuracy of these tools.
Key findings
- We observed that existing AI-assisted dietary assessment tools are integrated with mobile/web-based applications to provide a user-friendly interface.
- These tools can broadly be categorized as "Image-based" and "Motion sensor-based." Image-based tools allow food recognition, classification, food volume/weight, and nutrient estimation whereas, Motion sensor-based tools help capture eating occasions through wrist movement, eating sounds, jaw motion & swallowing.
- These functionalities capture the dietary data regarding the type of food or beverage consumed, calorie intake, portion sizes, frequency of eating, and shared eating occasions as real-time data making it more accurate as against conventional dietary assessment methods.
- Dietary assessment tools integrated with AI and ML could estimate real-time energy and macronutrient intake in patients with chronic conditions such as obesity, diabetes, and dementia.
- Additionally, these tools are non-laborious, time-efficient, user-friendly, and provide fairly accurate data free from recall/reporting bias enabling clinicians to offer personalized nutrition.
Why it matters
This review describes artificial intelligence-assisted dietary assessment tools that could estimate food and nutrient intake in people with chronic conditions including dementia. It reports no tested treatment, cure, or prevention, and the authors say more studies are needed to evaluate these tools.
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Study details
- Participants17,613
- Evidence strengthLow
How this study scored 49/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in Nutrition) |
| Journal's citation record | 1 / 4 | Frontiers in Nutrition: journal h-index 147 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 4 / 4 | 17,613 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 56 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 18.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 11.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 3 / 4 | Cited 56 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Devaki Gokhale: h-index 9 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Symbiosis International University: institution h-index 190 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39917741) |
| Free to read | 2 / 2 | Free to read |
| Total | 49 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fnut.2025.1518466
- PubMed: 39917741
- Original source →
- Free full text: Frontiers in Nutrition →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Phalle, A., & Gokhale, D. (2025). Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools—a scoping review of potential applications. Frontiers in Nutrition. https://doi.org/10.3389/fnut.2025.1518466
Vancouver
Phalle A, Gokhale D. Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools—a scoping review of potential applications. Frontiers in Nutrition. 2025. doi:10.3389/fnut.2025.1518466. PMID: 39917741.
BibTeX
@article{phalle2025-39917741,
author = {Phalle, Anuja and Gokhale, Devaki},
title = {{Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools—a scoping review of potential applications}},
journal = {Frontiers in Nutrition},
year = {2025},
doi = {10.3389/fnut.2025.1518466},
pmid = {39917741},
url = {https://doi.org/10.3389/fnut.2025.1518466}
}
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