Inspiration
The inspiration behind JARVIS HealthCare AI came from the growing need to enhance healthcare services with cutting-edge technology and the name came from (Iron-Man). With the increasing complexity of medical data and risk of skin cancer development, we wanted to create an AI-driven solution for healthcare professionals with an intelligent and interactive dashboard that could assist with diagnosing (detecting skin conditions from images), creates prescriptions, and personalizing healthcare decisions in a more efficient and accessible manner.
What it does
JARVIS HealthCare AI is a sophisticated AI-powered platform designed to assist individuals with clinical decision-making. It analyzes patient data, and imaging, to generate accurate predictions, suggest treatment options, and highlight potential risks. The AI continuously learns from new data, improving its recommendations over time.
How we built it
We built the Frontend using React.JS, a Javascript framework.
We built JARVIS HealthCare AI using a combination of PyTorch, AI, and Python, leveraging cutting-edge machine learning and deep learning technologies to create an advanced healthcare platform.
Deep Learning with PyTorch: At the heart of JARVIS HealthCare AI, we used PyTorch to develop and train complex models for detecting skin conditions from images.
Python: Python was used extensively throughout the project due to its robust ecosystem for data science, AI, and machine learning.
User Interface: On the front end, we focused on building an intuitive and interactive dashboard that healthcare providers could use seamlessly.
Challenges we ran into
We were trying to figure how exactly to train AI models to make it scan the image and prescribe the medication.
Accomplishments that we're proud of
We were able to integrate an AI image recognition model using backend frameworks like Pytorch allowing Jarvis to analyze and intepret images with impressive accuracy.
Developed a voice-enabled AI assistant that listens, understands, and follows user commands, creating a more natural and accessible experience for clinicians and patients alike.
We combined computer vision and natural language processing to build a healthcare AI solution that would potentially help people in the future.
What we learned
We learned how to train computer vision AI models.
What's next for JARVIS HealthCare AI
Reach out to health professionals to test our website and gather clinical feedback. Refine on the design to ensure it's intuitive and effective for healthcare professionals under pressure.
Log in or sign up for Devpost to join the conversation.