Project Overview
Project Summary:
Our project, ILLZZIK, is a web app that uses AI to diagnose skin diseases primarily for seniors suffering from regional medical disparities. Users can upload photos of skin conditions through the app, and the AI provides diagnoses. The app simplifies diagnosis explanations for easy understanding and supports voice input and output for convenience, enhancing accessibility to medical services for these vulnerable groups.
Identifying the Challenge
The Social Problem:
The primary social issue addressed by ILLZZIK is resolving regional healthcare disparities within an aging society. These disparities often result in inadequate medical access for seniors in remote or underserved areas. Early detection and prevention of diseases are crucial for these populations, making it essential to provide a solution that brings medical services closer to them, regardless of their geographical location.
Innovation and Uniqueness
Why Our Project Stands Out:
ILLZZIK distinguishes itself by addressing the crucial issue of healthcare accessibility through a unique blend of AI technology and user-centric design. The app's innovative use of AI to diagnose skin diseases and recommend specialists is especially designed for seniors, who are most at risk of being affected by regional healthcare disparities. Its voice input and output features ensure that even users with limited tech-savviness can easily access and use the app, making it a pioneering solution in mitigating healthcare access issues in an aging society.
Insights and Development
Learning Journey:
The journey of developing ILLZZIK illuminated the critical need for accessible healthcare solutions in aging and geographically diverse populations. One of the key challenges was designing an AI system capable of accurately diagnosing a wide range of skin conditions from varied user-taken photos. Additionally, making the app intuitive for seniors required innovative UI/UX approaches. Feedback loops and user testing were instrumental in overcoming these challenges, leading to significant improvements in AI accuracy and user interface design.
Development Process:
The creation of ILLZZIK leveraged Streamlit for its front-end, providing a rapid development environment conducive to creating interactive, user-friendly applications. Python was used for back-end development, with AI and machine learning models developed using TensorFlow, Keras, and other libraries. The development journey encompassed initial planning and design phases, focusing on user experience and AI model accuracy, followed by intensive testing and refinement stages. This iterative approach, enriched by user feedback and real-world testing, was crucial in evolving ILLZZIK into an effective tool for bridging healthcare access gaps, demonstrating the power of integrated technological solutions in confronting societal challenges.
