MediScribe AI

Inspiration

Doctors spend a significant amount of their time on documentation instead of patient care. Manual note-taking during consultations is time-consuming, error-prone and a major contributor to clinician burnout. At the same time, patients often leave appointments without fully understanding their diagnosis or treatment plan.

MediScribe AI was inspired by the need to reduce administrative burden for doctors while improving clarity and communication for patients. The goal was to bridge technology and medicine by transforming natural doctor–patient conversations into structured, usable clinical documentation.

What it does

MediScribe AI is an AI-powered medical documentation and workflow assistant. It allows healthcare professionals to: Input doctor–patient conversations via text or audio Automatically generate structured clinical notes (SOAP format) Produce patient-friendly summaries that are easy to understand Save and manage consultation records securely

The system helps doctors document faster, stay focused on patients and maintain consistent medical records, while helping patients better understand their care.

How I built it

MediScribe AI was built as a web-based prototype using: Frontend: HTML, CSS, JavaScript Backend: PHP Database: MySQL AI Processing: Featherless AI (for text analysis and structuring) Audio Processing: ElevenLabs (free tier) for speech-to-text simulation UI Design: Clean, minimal medical UI with clear workflow steps

The workflow:

Doctor inputs conversation text or records audio
Audio is transcribed into text
AI processes the text to extract symptoms, assessments and plans
Structured SOAP notes and patient summaries are generated
Records are stored securely for later access

Challenges I ran into

Designing a solution that is medically useful without accessing real patient data Ensuring the AI output is clear, structured and clinically meaningful Balancing technical feasibility with healthcare compliance considerations Building a complete system solo within a limited time frame Addressing data privacy and GDPR concerns in a healthcare context

Accomplishments that I'm proud of

Successfully built a working end-to-end prototype as a solo developer Created a solution that addresses a real healthcare pain point Designed a system that supports both doctors and patients Structured outputs in a clinically relevant format (SOAP notes) Ensured privacy-aware design by avoiding patient-identifiable data

What I learned

How complex and sensitive healthcare technology development is The importance of clarity, trust and usability in medical tools How AI can meaningfully support not replace medical professionals How to design solutions that are technically feasible and ethically responsible End-to-end product thinking: from idea to prototype to presentation

What's next for MediScribe AI

Future improvements include: Integration with Electronic Health Record (EHR) systems Multilingual transcription and summaries Improved medical entity recognition using specialized medical models Secure authentication and role-based access Clinical validation with healthcare professionals Full GDPR-compliant deployment with encrypted storage

MediScribe AI aims to evolve from a hackathon prototype into a scalable medical productivity tool that supports healthcare systems globally.