What it is
Source: Public repo ahdhi/clara-ai-health-diagnoser; the previous version of this site. Date: Repository commits from 25 September to 7 October 2025; read again on 15 October 2026. Scope: What Clara is and why its accuracy claim is struck through. No accuracy figure exists, and this page claims none. Not shown: An accuracy figure. None exists.
Clara is a health-diagnosis prototype: a web app built around Google’s Gemini model. Built The previous version of this site said it was 97 % accurate. It has not been measured, so the figure is struck through until it is. Method
What it does
None of these has been evaluated:
- Differential diagnosis. It asks Gemini which conditions could explain a patient’s symptoms, most likely first.
- Medical image analysis. It asks Gemini to interpret a medical image.
- Drug-interaction checks. It flags interactions between medicines from a built-in table, each with a severity.
- Diagnosis history. A signed-in user’s past analyses are kept in Firebase.
How it is built
It uses React 18, TypeScript, Vite and Tailwind CSS, with Firebase authentication and the Firestore database. The repository’s commits run from 25 September to 7 October 2025, and the prototype is live on Netlify. Built
Stack. React, TypeScript, Vite, Tailwind CSS, Firebase, Google Gemini
Where it stands
It is a prototype, not medical advice. The repository holds no labelled evaluation data and no held-out split. Method An earlier description of mine named a Random Forest, XGBoost and neural-network version, with no accuracy figure. The instrument below sets out what an honest evaluation needs.
My part
I designed and built Clara, and made all 11 commits in its public repository. Built
Clara, re-examined
Clara’s accuracy is not yet measured. The old claim is struck through until it is. Method
What an honest evaluation would be: a named dataset, a held-out split, a confusion table per condition, calibration, and a leakage check. Until those exist, the number is: not yet measured.
Pending. Awaiting material from Adhi; the Claim plane states exactly what.
Claim
Source: Public repo, ahdhi/clara-ai-health-diagnoser. Date: Read for this build, Oct 2026. Scope: The repository as it stands: React, TypeScript and Vite with Firebase, around Google Gemini; 11 commits; a README with claims it does not support; a demo link that points nowhere. Not shown: An accuracy figure. None exists.
97 % accuracy, PyTorch, BERT not yet measured Method
The old version of this site said that. The repository does not support it: there is no evaluation in it, no dataset, no held-out split, and no accuracy figure anywhere. An earlier description of mine named Random Forest, XGBoost and neural-network models with NLP symptom extraction; the code in the public repository is a React, TypeScript and Vite front end with Firebase, around Google Gemini. Neither description has been measured, so the honest number is: not yet measured.
Clara stays here as a prototype, and the claim stays struck through until an evaluation exists. The Method plane says what that evaluation would be.
Method
Source: Method, stated here. Date: This build. Scope: What would have to exist before any accuracy figure could be printed on this page.
An honest evaluation of a model that suggests conditions from symptoms would need, at minimum:
- A named dataset, with its licence and provenance stated, and a note of how the labels were made and by whom.
- A split made before any modelling: train, validation and a held-out test set, stratified by condition, with the test set touched once.
- A confusion table per condition on that test set, not a single accuracy figure, because accuracy hides which conditions are missed and which are over-called.
- Calibration: when the model gives a probability, that probability should match how often it is right; a reliability plot and a Brier score show whether it does.
- A leakage check: no record in the test set shares a source, a near-duplicate text or a label-revealing feature with the training set.
Until those five exist, the only accurate statement about Clara’s accuracy is that it has not been measured. A model that gives health suggestions should be held to this at least, and this page holds its own claim to it first.
Raw
Source: GitHub and Netlify. Date: Read for this build, Oct 2026. Scope: The public repository and the live prototype, as found.
The repository: github.com/ahdhi/clara-ai-health-diagnoser. React, TypeScript and Vite, with Firebase, around Google Gemini. 11 commits. The README carries claims the code does not support, and its demo link points to nothing. Built
The live prototype: clara-ai.netlify.app. It is a prototype, and it is not medical advice.
The previous site’s claim, as recorded: 97 % accuracy … PyTorch, BERT.
