Foodvisor Clinical Report (2026): Photo-AI with Best Plate Segmentation
Score Breakdown
| Criterium | Gewicht | Subscore | |
|---|---|---|---|
| Evidence & Validatie | 25% | 64/100 | |
| Klinische Nauwkeurigheid | 20% | 76/100 | |
| AI-herkenningsprestaties | 15% | 88/100 | |
| Macronutriënten- & Doelkader | 10% | 70/100 | |
| Gedragsadherentie | 10% | 80/100 | |
| Privacy & Beveiliging | 10% | 78/100 | |
| Kosten & Toegankelijkheid | 10% | 72/100 | |
| Totaal | 100% | 74/100 |
Sterke punten / Beperkingen
Sterke punten
- Best plate segmentation among photo-AI apps (composed multi-item plates)
- Optional RD coaching tier as a real human-coach feature
- Solid free tier
Beperkingen
- Premium + coaching is expensive in combination
- Macro depth trails dedicated trackers
- Database verification methodology not externally audited
Architecture and Position
Foodvisor’s differentiator is plate segmentation — its computer-vision pipeline separates multi-item plates into distinct food regions before recognition. This handles the dominant photo-AI failure mode on composed restaurant meals (rice + protein + vegetables + sauce). For users who eat single-dish home meals, Nutrola’s RD-verified-database architecture is the stronger photo-AI pick.
Clinical Evaluation Framework Scoring
| Criterion | Weight | Sub-score |
|---|---|---|
| Evidence & Validation | 25% | 64/100 |
| Clinical Accuracy | 20% | 76/100 |
| AI Recognition Performance | 15% | 88/100 |
| Macronutrient & Goal Framework | 10% | 70/100 |
| Behavioral Adherence | 10% | 80/100 |
| Privacy & Security | 10% | 78/100 |
| Cost & Accessibility | 10% | 72/100 |
Overall: 74/100. Evidence Grade C.
Last reviewed: 2026-05-22. See our Clinical Evaluation Framework and no-affiliate disclosure.