Skin AI Check API

API ID 12884

AI-powered skin condition detection from a photo. Send one image plus symptom context and receive the detected condition (80+ supported), a confidence score, and a plain-language summary. Validated across all Fitzpatrick skin types I–VI.

API Documentation

Endpoints

Request

Analyses a photo of a skin area and returns an AI assessment. Upload one image (JPG) as multipart/form-data, optionally adding symptom context: body location, duration, details, and how symptoms have changed over time. Returns the detected condition (result), a confidence percentage (confidence), and a plain-language explanation (summary) suitable for displaying to end users. Covers 80+ skin conditions and is validated across all Fitzpatrick skin types I–VI. This is a screening and informational tool, not a medical diagnosis.

Endpoint ID: 25652
POST https://zylalabs.com/api/12884/skin+ai+check+api/25652/detect+skin+condition
INPUT PARAMETERS

Detect Skin Condition — Endpoint Features

Object Description
skin_illness_location Optional Body location of the skin issue, e.g. face, arm, back
symptoms_duration Optional How long symptoms have been present, e.g. 2 weeks, 3 months
symptoms_details Optional Additional symptom details, e.g. itching, bleeding, pain
symptoms_change_over_time Optional How symptoms have changed, e.g. growing, darkening, spreading
Request Body Required File Binary

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

skin_illness_location
symptoms_duration
symptoms_details
symptoms_change_over_time
API EXAMPLE RESPONSE
JSON
{"result":"Not a Skin Image","confidence":99.0,"summary":"From this photo, I can see a low-resolution view of a child’s face and upper torso, but I do not see a clearly defined skin lesion. There are no obvious raised nodules, ulcers, crusting, grouped blisters, ring-shaped rash, or scaly plaques. I also do not see clear features that would suggest neurofibromatosis (such as multiple visible neurofibromas or prominent café-au-lait spots). Because the image does not show a specific lesion clearly, any automated “neurofibromatosis” result is not supported by visible findings here and may be a false positive for this image.\n\nYou mentioned a self-reported painful spot on the face that has been growing for about 2 weeks. If that is present, it may simply not be captured well in this photo. Consider taking a new, well-lit, in-focus close-up of the exact spot (and one from the side), and include a photo from a normal distance for location.\n\nGiven pain and growth over 2 weeks on the face, it’s important to have an in-person exam soon (primary care, pediatrician, or dermatology). Seek urgent care sooner if it becomes rapidly larger, very red/warm, drains pus, is near the eye, is associated with fever, or causes swelling or trouble breathing.","discordant":false}
Detect Skin Condition — CODE SNIPPETS

    curl --location 'https://zylalabs.com/api/12884/skin+ai+check+api/25652/detect+skin+condition?skin_illness_location=face&symptoms_duration=2 weeks&symptoms_details=pain&symptoms_change_over_time=growing' \
    --header 'Content-Type: application/json' \ 
    --form 'image=@"FILE_PATH"'

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Skin AI Check API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Starts at
$ 10,000/Year


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Overview

Skin AI Check API enables developers, clinics, insurers, wellness platforms, and digital health companies to integrate AI-powered skin image screening into their own applications.

HOW IT WORKS
Send a single photo of a skin area together with short symptom context (location, duration, details, change over time) via one multipart/form-data POST request. The API returns a structured JSON assessment: the detected condition, a confidence percentage, and a plain-language summary you can display directly to end users.

KEY FEATURES
- 80+ skin conditions detected, including common dermatological and high-risk conditions
- 95–97% accuracy on benchmark datasets 
- Validated across all Fitzpatrick skin types I–VI — built for diverse, global user bases
- Two-tier AI architecture: a vision model for detection plus a reasoning layer that produces clear explanations
- Combines image analysis with symptom context for higher accuracy than image-only APIs
- Works with any standard smartphone photo (JPEG/PNG)

 

TYPICAL USE CASES
- Telehealth and clinics: pre-consultation triage and patient self-checks
- Insurers and wellness platforms: preventive screening inside member apps
- Beauty and skincare brands: skin analysis that drives personalised product recommendations
- Digital health apps: add dermatology AI in one afternoon

 

IMPORTANT
Skin AI Check API is a screening and informational tool, not a medical diagnosis. Applications should always advise users to consult a healthcare professional.

Skin AI Check API FAQs

The Detect Skin Condition endpoint returns a structured JSON response containing three key pieces of information: the detected skin condition (result), a confidence percentage (confidence), and a plain-language summary (summary) that explains the assessment.

The key fields in the response data are "result," which indicates the detected skin condition; "confidence," which provides the likelihood of the condition being accurate; and "summary," which offers a user-friendly explanation of the findings.

The response data is organized in a JSON format with three main fields: "result" for the skin condition, "confidence" for the accuracy percentage, and "summary" for a plain-language explanation. This structure allows for easy parsing and display in applications.

The endpoint provides information on over 80 skin conditions, including common and high-risk conditions, along with a confidence score and a summary that contextualizes the findings based on the submitted image and symptom details.

Data accuracy is maintained through a two-tier AI architecture that combines a vision model for detection with a reasoning layer for clear explanations. The API has been validated across all Fitzpatrick skin types and adheres to clinical-grade standards.

Typical use cases include telehealth pre-consultation triage, preventive screening for insurers and wellness platforms, personalized skin analysis for beauty brands, and integration into digital health apps for dermatology AI features.

Users can customize their data requests by providing optional symptom context parameters, such as body location, duration, details, and changes over time, alongside the image upload to enhance the accuracy of the assessment.

Standard data patterns include a high confidence percentage (typically between 95-97%) for common conditions, along with clear summaries that explain the detected condition in layman's terms, making it accessible for end users.

The endpoint accepts a skin image in JPG format as multipart/form-data. In addition, users can include optional parameters that describe the context of the symptoms, such as location on the body, duration, details, and changes over time, to improve the accuracy of the evaluation.

Users can view the detected skin condition, the confidence percentage, and the summary in plain language directly in their applications. This allows end users to easily understand the assessment and make informed decisions about skincare.

The endpoint covers more than 80 skin conditions, including common and high-risk diseases. It provides a detailed assessment that includes the detected condition, the confidence in the evaluation, and a summary that contextualizes the findings based on the image and symptoms provided.

The quality of the data is ensured through a two-level AI architecture, which combines a vision model for detection with a reasoning layer. In addition, the API is validated across all Fitzpatrick skin types and follows rigorous clinical standards, such as ISO 13485 and ISO 27001.

The response is structured in JSON format, containing three main fields: "result" (skin condition), "confidence" (percentage of accuracy), and "summary" (explanation in simple language). This structure facilitates analysis and integration into applications.

If the API returns partial or empty results, users should check if the image and context parameters were sent correctly. It is recommended to reassess the quality of the image and provide additional information about the symptoms to improve the accuracy of the evaluation.

Data patterns include a high percentage of confidence, usually between 95-97%, for common conditions. The summaries are clear and accessible, allowing end users to easily understand the detected condition and its implications.

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