Scan Skin Analysis API

API ID 12885

Detect skin conditions effortlessly with AI-driven analysis.

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: 25653
POST https://zylalabs.com/api/12885/scan+skin+analysis+api/25653/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":"Normal Skin","confidence":92.0,"summary":"**What I can see in the photo (image-only observation, not a diagnosis):**  \nThe photo is taken from a distance and is low resolution, so fine skin details are hard to evaluate. On visual inspection of the child’s face and upper body, I do not see a clearly defined or sharply bordered skin lesion. There are no obvious clustered blisters, honey-colored crusting, ring-shaped rash, scaly plaques, open sores, or distinct raised nodules that would point to a specific skin condition. I also do not see clearly identifiable café-au-lait spots or multiple neurofibroma-like bumps in this image. Because the exact spot of concern is not clearly visible, this photo alone cannot confirm or rule out a problem.\n\n**How this fits with the reported symptoms (unverified):**  \nYou mentioned a **painful, growing facial spot for about 2 weeks**. Pain and growth can sometimes suggest inflammation or infection (such as an inflamed cyst, pimple/folliculitis, or an abscess), even if it is not obvious in this picture.\n\n**What to do now:**  \n- Arrange an **in-person medical evaluation soon (ideally within 24–48 hours)**, especially since it is **painful and enlarging** and on the **face**.  \n- Until then: avoid squeezing/popping it; use **warm compresses** 10–15 minutes, 3–4 times/day; keep the area clean; consider age-appropriate pain relief if needed.\n\n**Get urgent care now if any of these occur:**  \n- Fever, worsening swelling/redness, rapidly increasing size, pus/drainage, severe pain  \n- Redness/swelling near the eye, eyelid swelling, vision changes, or pain with eye movement  \n- The child seems unwell, has trouble breathing/swallowing, or the area becomes very tender and firm\n\n**If you can, share better photos:**  \nA **close-up, well-lit, in-focus** photo of the exact spot (plus one from farther away for location) will help with a more accurate visual assessment.","discordant":false}
Detect Skin Condition — CODE SNIPPETS

    curl --location 'https://zylalabs.com/api/12885/scan+skin+analysis+api/25653/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 Scan Skin Analysis 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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Overview

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

Scan Skin Analysis API FAQs

The Detect Skin Condition endpoint returns a structured JSON response containing three key fields: the detected skin condition (result), a confidence percentage (confidence), and a plain-language summary (summary) suitable for end users.

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

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 clear 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 dermatological issues. It also includes a confidence score and a summary to help users understand the assessment.

Users can customize their data requests by including optional parameters such as body location, duration of symptoms, specific details, and changes over time when submitting the image. This context enhances the accuracy of the assessment.

Data accuracy is maintained through clinical-grade standards, including ISO 13485 quality management and ISO 27001 information security. The API has been validated across all Fitzpatrick skin types and tested against benchmark datasets.

Typical use cases include telehealth pre-consultation triage, preventive screening in wellness apps, personalized skincare recommendations, and integration into digital health applications for enhanced dermatological insights.

Users can utilize the returned data by displaying the "result" to inform users of potential skin conditions, using the "confidence" to gauge the reliability of the assessment, and presenting the "summary" for clear communication of findings.

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