Generative AI Accelerator: Ignite Your Creative Tech Ideas

Describe your company in 1-2 sentences.

CoMed, Inc. (the company) is a software development company dedicated to creating innovative solutions that address critical needs within the healthcare industry. Our focus lies in developing advanced software that is designed to seamlessly integrate into the workflows of hospitals and clinics, enhancing the accuracy of diagnoses while fostering a supportive community among healthcare professionals and patients all over the globe.

Describe the problem you are solving or the business pain you are addressing.

During my last visit to a government hospital with my professor, I noticed long queues for Brain MRI results. Doctors struggle to provide timely conclusions due to the inefficiency of traditional methods, which rely heavily on time-consuming and error-prone radiologist interpretations. There is also a shortage of radiologists, especially in rural and underserved areas, limiting access to specialized healthcare services and continuous patient engagement.

Describe your competitive landscape and product differentiation.

Our focus is on enhancing diagnostic accuracy, reducing wait times, and enabling efficient workflows in healthcare. We address the shortage of specialists, especially in rural and underserved areas, by providing tools that deliver accurate results without the need for manual analysis. Our platform fosters community-driven treatment, enabling continuous patient engagement and seamless communication among patients, doctors, and hospitals. We build a connected healthcare ecosystem that prioritizes patient care and supports healthcare professionals globally.

Describe your current tech stack or approach (e.g., technologies, language, architecture overview). Describe what technologies you use, languages, etc. Along with a high-level overview of your architecture, if available.

Flutter for cross-platform mobile app development and FastAPI for scalable backend APIs. We used Python for backend development and AI model integration. We fine-tuned GPT-3.5-turbo for medical diagnostics. Firebase handles real-time data management and authentication. We used Redux for predictable and maintainable global state management and Figma for minimalistic and intuitive UI/UX design.

Why are you considering AWS for your Generative AI needs?

All we know is that AWS is a leader among Cloud Computing Platforms. And we will migrate to AWS. So we are considering AWS for our Generative AI needs due to several key factors that align with our goals and requirements:

Scalability: AWS offers highly scalable infrastructure, allowing us to handle varying loads and demands efficiently. This is crucial for our AI models, which require substantial computational resources, especially during peak times.

Comprehensive AI Services: AWS provides a wide range of AI and machine learning services, such as Amazon SageMaker, which streamlines the process of building, training, fine-tuning and deploying machine learning models. These services enable us to integrate advanced AI capabilities without extensive overhead.

High Performance and Reliability: AWS is known for its robust performance and reliability. Utilizing AWS ensures that our AI applications run smoothly and consistently, which is critical for healthcare diagnostics and patient care where uptime and speed are paramount.

Data Security and Compliance: AWS offers strong data security measures and compliance certifications that meet the stringent requirements of the healthcare industry. This is essential for us to ensure patient data privacy and regulatory compliance.

Global Reach: With AWS's global infrastructure, we can deploy our solutions closer to our users around the world, reducing latency and improving the user experience. This is particularly beneficial for reaching underserved communities in remote areas.

Cost-Effectiveness: AWS's pay-as-you-go pricing model allows us to manage costs effectively by paying only for the resources we use. This is advantageous for our startup as it provides financial flexibility and the ability to scale our operations as needed.

Integration Capabilities: AWS integrates seamlessly with various tools and services that we will use in our future tech stack. This includes integration with other cloud services, databases, and third-party applications, facilitating a smoother workflow and enhanced functionality.

Advanced Analytics: AWS provides powerful analytics services that enable us to gain insights from our AI models and data. This helps in continuously improving our models and making data-driven decisions to enhance our healthcare solutions.

We are considering AWS for our Generative AI needs due to its scalable infrastructure, comprehensive AI services like Amazon SageMaker, and high performance and reliability, crucial for healthcare diagnostics. AWS ensures data security and compliance, offers global reach, cost-effectiveness, seamless integration, and advanced analytics, making it ideal for our goals and requirements.

Describe the specific industries and use cases you are focused on with your Gen AI application.

Health is a top priority for everyone, which is why we focus on the healthcare industry, specifically targeting Brain MRI analysis. Our use cases include enhancing diagnostic accuracy, reducing analysis time, and addressing radiologist shortages in rural and underserved areas using AI models trained on high-quality medical datasets.

What are the key innovations or differentiators compared to other foundational models / applications?

AI-powered diagnostic accuracy, real-time data processing, and community-driven patient engagement. Unlike traditional applications, our solution reduces analysis time, addresses radiologist shortages, and provides continuous patient support, especially in rural and underserved areas. Our vision is to build a whole ecosystem and foster a patient-centric approach to the healthcare industry.

What is your team's experience and expertise in building and deploying large-scale AI systems?

We've hands-on experience in building, fine-tuning, and deploying AI models, as well as in app development and healthcare technology. We successfully fine-tuned our first model for medical diagnostics, achieving accuracy that surpasses GPT-4o, the latest and advanced model from OpenAI. In the coming year, we're planning to develop a custom model fully specialized for medical use cases.

What specific goals and milestones do you want to achieve by the end of the program?

We want to build very first version of Medicord. Powerful tool for continuous patient care, enabling seamless communication, treatment plan sharing, and progress monitoring within a secure online environment. Platform designed to connect patients, doctors, and hospitals. This strengthens doctor-patient relationships and facilitates proactive care. Connect patients facing similar health challenges.