Evaluating No-Code and Low-Code Platforms for Medical Image Classification: A Systematic Review

The adoption of artificial intelligence in clinical practice is often limited by the technical complexity of model development, particularly for medical professionals without programming expertise. This study aims to evaluate and compare existing no-code and low-code AI platforms for medical image classification, while also demonstrating to clinicians how AI tools can be practically implemented without having technical expertise. In this work, a systematic evaluation of such platforms applied to the classification of skin diseases based on dermoscopic images. A repository of 34 no-code and low-code platforms available on the internet was gathered. By setting specific criteria for inclusion, supporting image classification, being user-friendly, being useful in healthcare, and serving as a deployment solution, the AI research team narrowed down the list from 22 to 17 then just five platforms for further exploration. In this paper, a standardized dataset that includes around 8000 labeled thermoscopic images across eight disease categories has been used to compare the selected platforms in the literature. Teachable Machine had the lowest accuracy (85.2%) and the shortest training time, whereas Edge Impulse had the highest accuracy (89.9%), with the shortest training time, and Roboflow had the lowest accuracy (86.8%), with the longest training time. The key contributions of the study are a systematic survey of available no-code and low-code AI services to classify skin disease images, a performance analysis, training efficiency, and usability trade-offs, and practical recommendations on how clinicians and healthcare professionals can use AI tools in clinical and healthcare environments without having to possess advanced technical skills.