Building an AI Agent for Medical Use case

This project aims to develop a full-featured, AI-powered medical assistant platform for doctors to seamlessly handle patient visit documentation from start to finish. The system must enable a doctor to start and stop live audio recordings of patient consultations directly from a web dashboard. Once the recording is stopped, the audio is automatically sent to the backend for transcription, using a multi-language-capable engine (e.g., Whisper), ensuring accurate capture of medical discussions in real-world, multilingual scenarios. After transcription, the platform must intelligently generate a concise and readable summary of the conversation, breaking it down into essential medical categories such as reason for visit, symptoms, diagnosis, suggested treatments or medications, and follow-up actions. Additionally, the AI agent should automatically extract or suggest perception preferences (e.g., patient appears anxious, diabetic, fatigued), allowing the doctor to approve, reject, or manually select relevant tags from a curated list. Doctors must also be able to choose at-home remedy suggestions from a pre-built, editable list (e.g., rest, hydration, salt water gargle), which get added to the final summary. Once reviewed, the doctor should edit and approve the transcript and summary, triggering secure delivery to the patient via email and SMS, and saving the entire visit (audio, transcript, summary, tags) to the patient's permanent medical history in the database. The doctor must also be able to search and retrieve previous visit records, review notes, re-send summaries, and export data as needed. The platform must include user authentication, doctor-specific dashboards, and a scalable backend. The entire system will be deployed using Vercel (frontend), Render or Railway (backend), and Supabase or Firebase (database and storage).
It must be production-ready, privacy-compliant, and built for real-world use with full source code ownership and clean architecture