MeTTa Medical Guideline Chatbot (MMGC)

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Expert Rating 3.0
Lio Mou
Project Owner

MeTTa Medical Guideline Chatbot (MMGC)

Expert Rating

3.0

Overview

The MeTTa Medical Guideline Chatbot (MMGC) project aims to develop an intelligent chatbot system that provides accurate medical guidelines to healthcare professionals and patients. Utilizing the MeTTa programming language, the chatbot will interpret natural language queries, convert them into MeTTa queries, and retrieve relevant medical information from a structured knowledge base. This project will showcase MeTTa's capabilities in natural language processing, data querying, automated reasoning, and demonstrate its potential in creating valuable medical AI applications within the SingularityNET ecosystem.

RFP Guidelines

Develop interesting demos in MeTTa

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $100,000 USD
  • Proposals 21
  • Awarded Projects 4
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SingularityNET
Aug. 12, 2024

Create educational and/or useful demos using SingularityNET's own MeTTa programming language. This RFP aims at bringing more community adoption of MeTTa and engagement within our ecosystem, and to demonstrate and expand the utility of MeTTa. Researchers must maintain demos for a minimum of one year.

Proposal Description

Project details

Objectives

  • Develop a medical guideline chatbot system using the MeTTa programming language.
  • Demonstrate MeTTa's capabilities in natural language understanding, data querying, and procedural content generation.
  • Provide an educational and useful tool that aligns with SingularityNET's projects, such as Rejuve.bio.
  • Showcase MeTTa's potential to the developer community through comprehensive documentation and tutorials.

Project Description

The MMGC project focuses on creating a chatbot that can:

  • Understand Natural Language Queries: Interpret user questions about medical guidelines using MeTTa's pattern-matching capabilities.
  • Convert Queries into MeTTa Syntax: Translate natural language inputs into MeTTa queries for processing.
  • Retrieve and Provide Information: Access a structured medical knowledge base to provide accurate and relevant guidelines.
  • Offer Justifications: Generate runtime justifications for the information provided, enhancing transparency and trust.
  • Update Dynamically: Incorporate new medical information by updating its source code dynamically, ensuring up-to-date responses.

Technical Approach

  • Language Processing Module:

    • Implement natural language processing using MeTTa's rewriting rules.
    • Utilize parsing techniques to handle various query structures.
  • Knowledge Base Development:

    • Create a comprehensive medical knowledge base within MeTTa.
    • Integrate reputable sources such as WHO guidelines and peer-reviewed journals.
  • Automated Reasoning Engine:

    • Implement logical deduction to provide justifications for responses.
    • Use procedural content generation for dynamic information retrieval.
  • User Interface:

    • Develop a simple, user-friendly interface for interacting with the chatbot.
    • Ensure accessibility across different platforms (web, mobile).

Expected Outcomes

  • Functional Chatbot:

    • A working medical guideline chatbot implemented in MeTTa.
    • Capable of understanding and responding to user queries with justifications.
  • Open-Source Code (optional):

    • Code released in a public repository under an appropriate OSS license.
    • Modular and extensible codebase for future enhancements.
  • Documentation and Tutorials:

    • Comprehensive documentation for developers and users.
    • Recorded tutorials to aid in learning and collaboration.
  • Technical Report:

    • A detailed report summarizing development, experiments, and results.

Alignment with RFP Objectives

  • Demonstrates MeTTa's Capabilities:

    • Showcases data querying, automated reasoning, and procedural content generation.
  • Educational and Useful:

    • Provides a valuable tool for healthcare professionals and patients.
    • Enhances ongoing projects like Rejuve.bio by offering AI-driven research assistance.
  • Collaboration Potential:

    • Modular design allows integration with other SingularityNET initiatives.
    • Encourages community engagement and further development.

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

  • Total Milestones

    7

  • Total Budget

    $25,000 USD

  • Last Updated

    9 Dec 2024

Milestone 1 - Project Initiation and Planning

Description

Define the project scope objectives and technical requirements. Set up the development environment and tools. Align team roles and responsibilities.

Deliverables

Comprehensive project plan and timeline. Technical design documents outlining system architecture. Configured development environment ready for coding.

Budget

$2,500 USD

Success Criterion

Approval of the project plan by all stakeholders. Successful setup of all development tools and environments. Team members understand their roles and project objectives.

Milestone 2 - Natural Language Processing Module Development

Description

Develop the core NLP module to parse and interpret user queries. Implement MeTTa's pattern-matching capabilities for basic medical queries. Focus on high-frequency medical terms and phrases.

Deliverables

Functional NLP module handling essential medical queries. Test cases demonstrating parsing accuracy and reliability. Documentation of the NLP module's functions and usage.

Budget

$5,000 USD

Success Criterion

NLP module accurately interprets at least 85% of test queries. Successful conversion of natural language queries into MeTTa syntax. Module passes unit tests for various query structures.

Milestone 3 - Medical Guidelines Knowledge Base Development

Description

Create a foundational medical knowledge base within MeTTa. Integrate a curated set of authoritative medical guidelines (e.g. common conditions). Ensure data is structured for efficient querying.

Deliverables

Initial knowledge base covering key medical topics. Data source documentation and knowledge base schema. Verification of data accuracy and reliability.

Budget

$6,000 USD

Success Criterion

Chatbot retrieves correct information for at least 80% of test queries. Knowledge base integrates seamlessly with the NLP module. Data passes validation against trusted medical sources.

Milestone 4 - User Interface Development

Description

Develop a simple user-friendly web interface for the chatbot. Implement basic features: query input response display and session management. Ensure responsive design for accessibility on various devices.

Deliverables

Functional web-based UI for user interaction. Basic styling and navigation elements. User feedback mechanisms (e.g. ratings comments).

Budget

$3,000 USD

Success Criterion

Users can input queries and receive responses without technical issues. UI is intuitive based on user testing with at least 5 individuals. Interface operates smoothly on common web browsers.

Milestone 5 - Integration and Testing

Description

Integrate the NLP module knowledge base and user interface into a cohesive system. Perform comprehensive testing including functional integration and user acceptance tests. Identify and fix critical bugs and performance issues.

Deliverables

Fully integrated chatbot system ready for deployment. Detailed test reports documenting testing procedures and results. List of resolved issues and improvement recommendations.

Budget

$4,000 USD

Success Criterion

System passes all critical test cases with a success rate of at least 90%. No major bugs remain that hinder basic functionality. Positive feedback from a pilot user group of at least 5 users.

Milestone 6 - Documentation and Tutorial Creation

Description

Prepare essential documentation for end-users and developers. Create basic tutorials and guides demonstrating how to use and modify the chatbot. Document code with comments and explanations for clarity.

Deliverables

User manuals and quick-start guides. Developer documentation including code comments and API references. Tutorial videos or step-by-step written guides.

Budget

$2,000 USD

Success Criterion

Documentation is clear and comprehensible, as confirmed by at least two external reviewers. Tutorials enable new users to interact with the chatbot without assistance. Developers can understand and modify the codebase using the provided documentation.

Milestone 7 - Deployment and Maintenance

Description

Deploy the chatbot on a Hyperon instance hosted by SingularityNET. Set up basic monitoring and maintenance protocols. Provide initial support and troubleshoot deployment issues.

Deliverables

Live chatbot accessible to intended users. Deployment and maintenance documentation. Initial performance metrics and user feedback collection setup.

Budget

$2,500 USD

Success Criterion

Chatbot is live and functioning correctly on the Hyperon platform. Users can access the chatbot without encountering deployment-related issues. Maintenance plan established for ongoing support.

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

3.0

  • Compliance with RFP requirements 4.0
  • Solution details and team expertise 3.7
  • Value for money 2.7
  • Expert Review 1

    Overall

    3.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 3.0
    • Value for money 0.0
    Promising but requires stronger technical depth.

    Practical proposal for a medical chatbot leveraging MeTTa’s data querying and reasoning capabilities. While the concept aligns with RFP, approach to converting NL to MeTTa syntax is unclear, especially given known challenges with LLMs doing this. Team shows general AI expertise, but clarity on mplementation and handling of complex medical queries is needed. Promising but requires stronger technical depth.

  • Expert Review 2

    Overall

    2.0

    • Compliance with RFP requirements 3.0
    • Solution details and team expertise 2.0
    • Value for money 0.0

  • Expert Review 3

    Overall

    4.0

    • Compliance with RFP requirements 4.0
    • Solution details and team expertise 3.0
    • Value for money 0.0
    It's a good demo idea, though the right way to achieve it might be a little different from what the proposer suggests

    I think this could be a good demo and it would be nice to have someone passionate for the domain playing with it. The right way to make it work would need some experimentation. It might be more like: build an appropriate medical ATomspace, then use some fancy forms of pattern-matching-fueled GraphRAG++-esque methods to enhance and fact-check an LLM doing the bulk of the chat... maybe using Metta-Motto to glue the pieces together. But SNet s bio -AI and semantic-parsing teams could work with the proposer to hash out the best detailed technical direction...

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