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You can run image inference in a browser with Transformers.js and WebGPU, but that does not make a skin-screening app “100% private” or medically reliable. Inference can happen on the user’s device while the app still fetches model files online—or sends images and metadata through its own code. And a general image classifier is not a skin-cancer diagnostic tool.
What this app can—and cannot—do
Transformers.js runs pretrained models in browser environments using ONNX Runtime, and its API includes an image-classification pipeline. Its WebGPU guide demonstrates selecting device: "webgpu" for inference. Those capabilities can support a technical demonstration that classifies an image in a browser; they do not establish that a particular model was trained or validated to assess skin lesions.
A classifier produces labels and scores associated with its model. A label that sounds medical does not turn the result into a diagnosis, and a score is not automatically a calibrated probability of disease. Do not present a generic model’s output as detecting, excluding, or confirming skin cancer.
How to build a browser inference prototype
Load a model with Transformers.js
Install Transformers.js in your web project, then create a pipeline for a model you have deliberately selected and reviewed. This minimal pattern shows where the WebGPU choice belongs; modelId must be replaced with the identifier for the model you intend to run.
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import { pipeline } from '@huggingface/transformers';
const modelId = 'your-reviewed-model-id';
const classifier = await pipeline('image-classification', modelId, {
device: 'webgpu'
});
const predictions = await classifier(imageElement);
console.log(predictions);The example follows the documented pipeline pattern, but it does not specify a skin-lesion model or validate one. Choose a model whose intended task, license, provenance, input requirements, and suitability for your use case you can verify. Hugging Face’s WebGPU example uses MobileNetV4 for general image classification, which is not evidence of skin-lesion training or clinical performance.
Handle unavailable or failing WebGPU
WebGPU availability varies by browser, version, operating system, and device. Transformers.js documentation reported global support of around 85% as of March 2026, based on Can I Use data; that dated estimate is not a guarantee for your users. The documentation also cautions that “The WebGPU API is still experimental in many browsers.”
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Check for the browser API, but do not treat that check as proof that the selected model will initialize or run successfully. A practical app should handle both capability and runtime failures. If you offer a fallback, test it with the selected model and target devices rather than assuming all runtimes support the same model.
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let classifier;
try {
if (!('gpu' in navigator)) throw new Error('WebGPU unavailable');
classifier = await pipeline('image-classification', modelId, {
device: 'webgpu'
});
} catch {
classifier = await pipeline('image-classification', modelId, {
device: 'wasm'
});
}This illustrates a fallback path, not a guarantee that a given model, browser, or device will work with either runtime. Show a clear error if initialization and fallback both fail; do not silently imply that an image was assessed when inference did not complete.
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Does browser inference keep skin photos private?
Not by itself. “Runs locally” describes where inference executes, not every movement of data in the application. Separate three questions:
- Where does inference happen? With a working WebGPU pipeline, the browser can perform model inference on the device.
- Where does the model come from? Transformers.js downloads model files from Hugging Face Hub and caches them in the browser by default. Model downloads are network activity even when the user’s image is not sent to an inference server.
- What does the app transmit? Upload handlers, analytics, crash reporting, logging, third-party scripts, or other application code could transmit images or related data. The library’s use does not establish what a particular app sends.
To substantiate a privacy claim, inspect the complete application and verify its behavior, including network requests during image selection and inference. Document whether images, predictions, identifiers, or diagnostic metadata leave the device; how model files are fetched and cached; and whether any retention occurs. Do not claim that this specific app keeps images on-device without evidence from its code and network behavior. “100% private” is an absolute claim that browser-side inference alone cannot support.
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Why an image-classification demo is not a skin-screening product
Skin-lesion interpretation is a health-related use, not a safe inference from a generic classification example. In the United States, FDA materials explain that software that acquires, processes, or analyzes a medical image may perform a device function; whether a product is regulated depends on its function and intended use. A machine-learning library does not establish clinical validation or FDA authorization.
The FDA’s classification for a software-aided adjunctive diagnostic device for suspicious skin lesions describes prescription use by a physician as a second read after the physician has already identified a suspicious lesion. It is not intended for standalone diagnosis or to confirm a clinical diagnosis. The FDA’s De Novo record for DermaSensor lists a decision date of January 12, 2024; that physician-facing regulatory example does not validate a consumer phone app or another model.
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The American Academy of Dermatology advises stringent scientific testing for diagnostic skin apps, including testing across skin tones, and highlights privacy protections. Its consumer guidance reports that apps designed to diagnose melanoma missed 41% of melanomas in studies cited on that page. That is the AAD’s summary of those studies, not a current universal performance estimate for every app or a result for this prototype. The AAD’s guidance is: “To protect your skin’s health, see a board-certified dermatologist for a diagnosis.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What clinical validation would need to establish
If the goal is more than a technical demo, evaluation must match the product’s intended users, setting, and claims. A single aggregate score cannot show that a model works reliably for the people and lesions it is meant to assess. FDA materials warn that limited representation of skin phototypes and lesion types in development data can limit generalizability.
- Define the intended use: specify who uses the tool, what images and lesion types it covers, and what action its output is meant to support. Claims and regulatory obligations depend on intended function.
- Use appropriate reference labels: establish how the clinical ground truth is determined for the intended task; a model’s own labels are not a reference standard.
- Represent intended populations: include relevant skin tones or phototypes and lesion types, and report performance for those groups rather than relying only on an overall metric.
- Separate evaluation data: use distinct training, validation, and test sets, with test data kept separate from model development, and evaluate in the patient groups in which the product is expected to be used.
- Make claims match evidence: do not describe a prototype as clinically validated, diagnostic, or FDA-authorized without product-specific evidence and, where applicable, the relevant regulatory basis.
These are evaluation requirements to plan for, not proof that a model has met them. The available technical example and general library documentation do not establish performance for any skin-screening use.
Quick Recap
Practical release checklist
- State plainly that a prototype’s image labels are not a diagnosis and do not use it to rule out disease.
- Test WebGPU initialization and any fallback on the actual browsers, versions, and devices you intend to support.
- Inspect network traffic and application code before making claims about image handling or privacy.
- Identify the model and establish its task, data provenance, limitations, and license before shipping it.
- For a clinical product, define intended use, obtain appropriate clinical evidence across intended groups, and determine applicable regulatory requirements before making diagnostic claims.
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