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International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 04 | Apr 2025

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e-ISSN: 2395-0056 p-ISSN: 2395-0072

MedAI Sandesh Ghorpade

Deepak Bonagiri

Department of Information Technology K.C. College Of Engineering

Asst.Prof. Priyanka Sananse

Aniruddha Keskar

Department of Information Technology Department of Information Technology K.C. College Of Engineering K.C. College Of Engineering ---------------------------------------------------------------------***--------------------------------------------------------------------and precision. This paper explores the implementation, challenges, and performance of MedAI in real-world scenarios, emphasizing its potential applications in telemedicine and remote healthcare. This project introduces a web-based AI/ML system for skin disease detection that allows users to upload images of affected skin areas. The system uses a trained convolutional neural network (CNN) to analyze the image and detect possible skin conditions in real time. Upon detection, it provides suggested medications, along with nearby clinic information based on the user’s location. What makes this solution unique is that it operates without the need for a doctor, making it especially useful for areas with limited healthcare facilities. The platform is user-friendly, privacy- conscious, and designed to deliver quick and reliable results, thereby promoting early detection, treatment guidance, and better health outcomes.

Abstract - Skin diseases pose a significant global health

challenge, impacting millions of people across various age groups and populations. Timely detection and precise diagnosis are essential for successful treatment and better patient outcomes. MedAI is an innovative, AI-driven system that leverages deep learning to classify and diagnose skin diseases with high precision. This research paper presents a comprehensive exploration of MedAI’s methodology, dataset, model architecture, and performance evaluation. The system shows strong potential in supporting dermatologists, minimizing diagnostic errors, and improving access to dermatological care especially in underserved areas. Additionally, the paper explores the ethical challenges of implementing AI in medical diagnostics, such as data privacy, model transparency, and seamless integration with current healthcare systems. By combining cutting-edge technology with a focus on scalability and inclusivity.

1.1 Literature Survey

1.INTRODUCTION

In 2025, a study published in the International Journal of Research Publication and Reviews explored the use of Convolutional Neural Networks (CNNs) for skin disease detection. The model achieved an impressive 93.5% F1score around multi-class skin disease classification tasks. The system focused on preprocessing techniques such as image normalization and augmentation to improve model accuracy. [1]

Skin diseases cover a broad range of conditions, from common issues like eczema and acne to serious, potentially fatal illnesses such as melanoma. Accurate diagnosis often requires the expertise of dermatologists, whose availability is limited, especially in remote and underdeveloped areas. The global prevalence of dermatological conditions, coupled with the shortage of specialists, has created an urgent need for scalable, accurate, and accessible diagnostic solutions. MedAI, an AIdriven system powered by deep learning, addresses this challenge by automating skin disease classification and providing treatment recommendations. By leveraging convolutional neural networks (CNNs), MedAI aims to reduce diagnostic errors, improve patient outcomes, and bridge the healthcare accessibility gap. Traditional diagnostic methods rely heavily on manual visual assessments and invasive procedures such as biopsies, which can be time-consuming, costly, and prone to human error. The incorporation of AI into medical imaging has shown impressive promise in automating processes and improving diagnostic accuracy. Building on this foundation, MedAI leverages deep learning techniques to classify skin diseases with both efficiency © 2025, IRJET

Impact Factor value: 8.315

A March 2025 paper in Biomedical Signal Processing and Control presented a novel use of transformer-based architectures for skin lesion classification. Unlike CNNs, transformers provided attention-based localization, improving the model’s focus on the actual disease regions..[2] A 2024 article in the Journal of Engineering and Applied Science proposed a deep learning framework for automated skin cancer screening using pretrained models like InceptionV3 and ResNet50. The system efficiently differentiated between benign and malignant skin conditions such as melanoma.[3]

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