Too little checked to say Only 95 reviews read, too few to say.
From our automated analysis, 27 Oct 2026.
This analysis describes version 1.5.4 of the app, analyzed 2026-10-27 .
The store currently lists version 1.5.4, updated 2026-10-13.
Store screenshots supplied by the developer.
Looking for a simpler way to log meals and track your nutrition? Lean is an AI-powered nutrition tracker designed to help you record meals, estimate calorie intake, and track macros. Whether your goal is weight management, muscle gain, or understanding your eating habits, Lean helps you keep track of your daily nutrition.
Track Your Meals with AI
Eating out or cooking at home? Lean uses AI to analyze food photos and provide estimated calorie information. Snap a photo, scan a barcode, or manually enter your meals to keep your food log in one place.
AI-Powered Features for Food Tracking…
From the developer’s store description.
For developers and the curious
The evidence, sources, measurements and versions behind every label above.
Facts that add context without being a charge.
In-app purchases run from USD 2.99 to USD 59.99 — a 20x spread across 10 items
The disclosed in-app purchases span an unusually wide price range. Published as context: the cheapest and priciest items may be different kinds of product entirely, so the spread alone deducts nothing.
Open observations that have not cleared the evidence bar.
The store privacy disclosure (5 entries) links no contact details or identifiers to the user — consistent with an app that does not require an account. Self-reported by the developer.
This observation has not cleared the required evidence standard.
The app's own screens offer a way past sign-up — guest access, skipping, or browsing before registering — rather than gating the product behind an account. The store's privacy disclosure is checked alongside it: that disclosure is self-reported, so it corroborates this credit rather than establishing it.
Nothing was published as a thing to watch for in this assessment. That is not a finding that nothing is wrong with the app.
No review-based observation is published in this assessment. That does not establish that reviewers had no problems.
“Not assessed” means we could not measure it. It is never a zero and never a pass.
No package has been scanned for this app, so nothing is published about what it ships. That is an absence of evidence, not a finding.
Supporting measurements: not a complete list of possible harms or positives. Measurement vocabulary 1.4 · how these are measured
This analysis describes version 1.5.4 of the app, analyzed 2026-10-27 .
The store currently lists version 1.5.4, updated 2026-10-13.
Each store’s release notes, as the developer wrote them. They are the developer’s words, not ours.
Bug fixes and performance improvements.
Appworthy
calorie-counter-by-lean-aiAssessed 2026-10-27 from 95 user reviews, the store listing and its privacy label. We have not scanned the app's package, so nothing on this page was read from inside the app itself.
What the stores publish. Store ratings are the stores’ numbers, not Appworthy’s, and are never averaged across stores.
From the store listing. Billing terms are read from each purchase’s name — the stores don’t publish them — so treat them as a strong hint, not a disclosure.
These are really on sale; we just can’t tell from the name whether they recur.
This assessment is produced automatically. Three independent signals are read — user reviews, the store listing and the store privacy disclosure — and each candidate finding is filed under the sources that support it.
No charge stands on one kind of evidence — with one published exception. A finding is either a measured fact that anyone can reproduce (an ad SDK is in the package; every subscription bills weekly; the privacy label omits a tracker we can see), or it is an interpretation — and an interpretation needs independent sources to agree before it reaches the catch list. The exception is a harm only users can see: a charge may publish from reviews alone, as “Reported by N users”, once enough reviewers raise it to clear that charge’s own published bar. That agreement rule is what replaced the human reviewer, not a model’s confidence.
There is no overall score, on purpose. A 0–99 number was published here until 2026-09-15. It was withdrawn because it discriminated too weakly to be the instrument — 44.7% of every pair of apps tied — and because a single number is a verdict where a count is an observation. What publishes instead is a row of measurements, each of which says what was counted and, where we could not look, says that instead.
Findings under One source only are real reports we do not stand behind as findings. They are shown because withholding them entirely would hide a genuine complaint, and labelled because publishing them as charges would be an accusation off a single source.
Write to [email protected], or use the form. Name the finding, the app version and the evidence that needs correcting. Factual corrections are free and never depend on buying anything.