Medical AI and First Response Assistant - AI Project Idea

Optimizing and Fine-Tuning DeepSeek R1 Model Using LoRA on PubMed, MedQA, and BioASQ; Designing a Flutter-Based GUI Integrating Whisper Voice Input and Coqui TTS; Enabled Dual Operational Modes for Emergency Response and Medical Queries.

1. Project Overview

1.1 Project Title: Medical AI and First Response Assistant

1.2 Purpose

The purpose of this project is to develop an AI-powered assistant that serves dual purposes:

• Emergency Response Mode: Provide rapid, accurate first-response guidance in critical medical situations.

• Medical Query Mode: Answer detailed medical questions for educational or informational purposes.

The project involves optimizing and fine-tuning the DeepSeek R1 model using Low-Rank Adaptation (LoRA) on medical datasets (PubMed, MedQA, BioASQ) and building a user-friendly Flutter-based graphical user interface (GUI) with Whisper for voice input and Coqui TTS for text-to-speech output.

1.3 Learning Objectives

• Gain hands-on experience in fine-tuning large language models (LLMs) using LoRA.

• Understand medical dataset preprocessing and model optimization.

• Develop skills in cross-platform app development with Flutter.

• Integrate speech recognition (Whisper) and text-to-speech (Coqui TTS) into a functional GUI.

• Apply AI in real-world medical and emergency response scenarios.

1.4 Target Audience

• Students with intermediate knowledge of AI/ML, programming (Python, Dart), and GUI development.

• Intended end-users: Medical professionals, first responders, and students.

2. Scope

2.1 In-Scope

• Fine-tuning the DeepSeek R1 model using LoRA on PubMed, MedQA, and BioASQ datasets.

• Designing and implementing a Flutter-based GUI with two operational modes:

  • Emergency Response Mode

  • Medical Query Mode

• Integrating Whisper for voice input and Coqui TTS for audio output.

• Testing the system for accuracy, usability, and responsiveness.

2.2 Out-of-Scope

• Deployment of the application to app stores.

• Real-time integration with live medical databases or hardware devices (e.g., wearables).

• Training the DeepSeek R1 model from scratch (pre-trained model will be provided).

• Multi-language support beyond English.

3. Functional Requirements

3.1 Model Optimization and Fine-Tuning

• Dataset Preparation:

  • Preprocess and clean data from PubMed (research articles), MedQA (medical Q&A), and BioASQ (biomedical Q&A).

  • Ensure datasets are formatted for compatibility with DeepSeek R1 and LoRA.

• Fine-Tuning with LoRA:

  • Use LoRA to efficiently fine-tune DeepSeek R1 for medical domain knowledge.

  • Optimize model performance for accuracy and response time.

• Evaluation:

  • Test model accuracy on a validation subset of MedQA and BioASQ.

  • Achieve at least 80% accuracy on medical question-answering tasks.

3.2 GUI Development

• Framework: Use Flutter for cross-platform compatibility (Android, iOS, and desktop).

• Modes:

  • Emergency Response Mode:

    • Quick, concise responses for first-aid instructions (e.g., CPR, choking).

    • Voice-activated input and audio output prioritized for hands-free operation.

  • Medical Query Mode:

    • Detailed responses to medical questions (e.g., symptoms, drug interactions).

    • Support for both text and voice input/output.

• Features:

  • Clean, intuitive interface with mode-switching toggle.

  • Real-time voice input using Whisper API/model.

  • Audio output of responses using Coqui TTS.

  • Error handling for poor voice input or model failures.

3.3 Integration

• Connect the fine-tuned DeepSeek R1 model to the Flutter GUI via an API or local inference.

• Ensure seamless interaction between Whisper (input), DeepSeek R1 (processing), and Coqui TTS (output).

4. Non-Functional Requirements

4.1 Performance

• Model response time: < 3 seconds for emergency mode, < 5 seconds for query mode.

• GUI load time: < 2 seconds on standard hardware (e.g., mid-range smartphone or laptop).

4.2 Usability

• Interface should be simple and accessible for non-technical users.

• Voice commands must be recognized with >90% accuracy in quiet environments.

4.3 Scalability

• System should handle up to 10 concurrent users in testing without performance degradation.

4.4 Reliability

• Model should provide medically accurate responses in >80% of test cases.

• GUI should not crash during voice input/output operations.

5. Technical Requirements

5.1 Tools and Technologies

• AI/ML:

  • DeepSeek R1 (pre-trained model).

  • LoRA for fine-tuning.

  • Python, PyTorch, or TensorFlow for model training.

• Datasets: PubMed, MedQA, BioASQ (provided or accessible online).

• GUI:

  • Flutter (Dart) for cross-platform development.

  • Whisper (open-source speech-to-text).

  • Coqui TTS (open-source text-to-speech).

• Hardware:

  • GPU-enabled system for model training (e.g., NVIDIA GPU).

  • Standard laptop/smartphone for GUI testing.

5.2 Dependencies

• Pre-trained DeepSeek R1 model availability.

• API keys or local setup for Whisper and Coqui TTS.

• Stable internet for dataset downloads and potential API calls.

6. Deliverables

6.1 Code

• Python scripts for dataset preprocessing and model fine-tuning.

• Flutter codebase for the GUI application.

• Integration scripts (e.g., API or local inference setup).

6.2 Documentation

• Technical Report:

  • Overview of model fine-tuning process and results.

  • GUI design and implementation details.

  • Challenges faced and solutions implemented.

• User Manual: Instructions for installing and using the application.

6.3 Demonstration

• A working prototype showcasing both modes (emergency and query).

• Video or live demo of voice input/output functionality.

7. Timeline

Phase	Tasks	Duration	Deadline
Phase 1: Research	Study DeepSeek R1, LoRA, Flutter, Whisper, Coqui TTS	1 week	Apr 16, 2025
Phase 2: Data Preparation	Preprocess PubMed, MedQA, BioASQ datasets	1 week	Apr 23, 2025
Phase 3: Model Fine-Tuning	Fine-tune DeepSeek R1 using LoRA	2 weeks	May 07, 2025
Phase 4: GUI Development	Build Flutter GUI with mode-switching	2 weeks	May 21, 2025
Phase 5: Integration	Integrate model, Whisper, and Coqui TTS	1 week	May 28, 2025
Phase 6: Testing & Refinement	Test system, fix bugs, optimize performance	1 week	Jun 04, 2025
Phase 7: Documentation	Write technical report and user manual	1 week	Jun 11, 2025
Phase 8: Submission	Submit deliverables and demo	-	Jun 12, 2025

Note: The timeline date is provided for your convenience in planning; you can adjust it as needed and begin working on this project accordingly.

8. Assumptions

• Students have basic knowledge of Python, Dart, and AI/ML concepts.

• Access to necessary hardware (GPU for training, laptop/phone for GUI).

• Datasets and pre-trained models are available or downloadable.

9. Constraints

• Limited to open-source tools and datasets (no proprietary software).

• Project must be completed within the 8-week timeline.

• Team size: 1-4 students (adjust scope based on team size).

10. Success Criteria

• Fine-tuned model achieves >80% accuracy on medical Q&A tasks.

• GUI successfully switches between emergency and query modes.

• Voice input/output works seamlessly in a demo environment.

• All deliverables (code, documentation, demo) submitted by Jun 12, 2025.

11. Support and Resources

• Mentor/Instructor: Available for weekly check-ins and troubleshooting.

• References:

  • DeepSeek R1 documentation.

  • LoRA research papers/tutorials.

  • Flutter, Whisper, and Coqui TTS official docs.

• Community: Online forums (e.g., Stack Overflow, GitHub issues).