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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Evaluating No-Code and Low-Code Platforms for Medical Image Classification: A Systematic Review.

Arwaa Wshyar Abdulkareem1, Aram Mahmood Ahmed2, Bryar Ahmad Hassan2,3

  • 1Artificial Intelligence and Innovation Centre, University of Kurdistan Hewlêr, Erbil, Iraq. arwaa.wshyar@ukh.edu.krd.

Journal of Imaging Informatics in Medicine
|March 30, 2026
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Summary
This summary is machine-generated.

This study evaluates no-code and low-code artificial intelligence (AI) platforms for skin disease classification using dermoscopic images. Edge Impulse achieved the highest accuracy, demonstrating practical AI implementation for clinicians without technical expertise.

Keywords:
Image classificationLow-code AINo-code AIPlatform evaluationSkin disease classification

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Dermatology

Background:

  • Adoption of AI in clinical practice is hindered by technical complexity for non-programmers.
  • Medical professionals require accessible AI tools for image classification tasks.
  • No-code and low-code platforms offer potential solutions for integrating AI in healthcare.

Purpose of the Study:

  • To evaluate and compare no-code and low-code AI platforms for medical image classification.
  • To demonstrate practical AI implementation for clinicians lacking technical expertise.
  • To assess the usability and performance of AI tools in classifying skin diseases from dermoscopic images.

Main Methods:

  • Systematic evaluation of 34 no-code/low-code AI platforms.
  • Inclusion criteria focused on image classification, user-friendliness, healthcare utility, and deployment.
  • Five platforms were selected for comparative analysis using a standardized dataset of ~8000 labeled dermoscopic images across eight disease categories.

Main Results:

  • Edge Impulse achieved the highest accuracy (89.9%) with the shortest training time.
  • Teachable Machine showed the lowest accuracy (85.2%) but had the shortest training time.
  • Roboflow had lower accuracy (86.8%) and the longest training time among the evaluated platforms.

Conclusions:

  • No-code and low-code AI platforms can be effectively used by clinicians for skin disease image classification.
  • Performance metrics like accuracy and training time vary significantly across platforms.
  • Practical recommendations are provided for healthcare professionals to leverage AI tools without advanced technical skills.