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Muhammad Anas Kamran@AgentArchitect
1
Events attended
1
Submissions made
Pakistan
1 year of experience
About me
I am an aspiring full-stack developer and AI enthusiast specializing in bridging complex backend architectures with intuitive, user-centric design. Currently a Computer Science student, I build robust applications using technologies like Java, Spring Boot, PostgreSQL, and JavaScript. Having recently completed the Google AI Professional Certificate, I am deeply passionate about integrating generative AI into practical workflows. Most recently, I served as Team Lead to develop a multimodal healthcare application for the AMD Developer Hackathon Act II. With a strong eye for graphic design and UI/UX paired with solid backend engineering skills, my goal is to lead teams in building scalable, enterprise-grade solutions that tackle real-world challenges.
🤝 Top Collaborators
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🤓 Latest Submissions
Medical Copilot: The Invisible Clinical Assistant
The Problem: Modern healthcare is facing an administrative crisis. For every hour a physician spends interacting with a patient, they spend up to two hours manually typing clinical notes, reviewing unstructured lab results, and navigating clunky Electronic Health Record (EHR) systems. This administrative fatigue directly leads to physician burnout and increases the risk of clinical errors. The Solution: Medical Copilot is an "invisible assistant" designed to let doctors be doctors again. Instead of clicking through drop-down menus, a physician simply hits record and dictates their notes naturally, or uploads a patient’s medical PDF. Our application handles the rest. How It Works: Built with a sleek, dark-themed React frontend, the UI features real-time audio waveform visualization and a seamless patient lookup gate. Once data is captured, it is routed through a complex n8n backend architecture. Leveraging the Fireworks AI API (in alignment with the AMD Developer Hackathon requirements), the backend engine performs rapid, low-latency extraction of critical medical entities. It automatically identifies: Prescribed medications (parsing names, dosages, and frequencies). Potential safety warnings and drug interactions. Concise clinical summaries. The Output: The extracted data is returned to the frontend for quick physician review and editing. With a single click, the doctor can generate a printable PDF report or approve the data to be pushed directly into a simulated Google Sheets EHR database. The app also features a full patient history retrieval system, providing a complete, closed-loop enterprise solution. Medical Copilot doesn't just wrap an AI in a chatbox; it delivers a production-ready, end-to-end pipeline that transforms unstructured clinical data into actionable, database-ready insights in seconds.
13 Jul 2026
