rajkstats / pharmassistai

An innovative application designed to help pharmacists and pharmacy students quickly research FDA-approved drugs by retrieving relevant information from drug labels and adverse event datasets, and providing AI-generated summaries to streamline the learning process

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PharmAssistAI	pharmassist.jpg	😻	green	yellow	docker	false	openrail

PharmAssistAI: Your Advanced Pharma Research Assistant

PharmAssistAI revolutionizes how pharmacy professionals and students approach learning and research related to FDA-approved drugs. By integrating modern information retrieval technologies with Large Language Models (LLMs), PharmAssistAI optimizes the research and learning workflow, making it less time-consuming and more efficient.

Core Features

• Comprehensive Data Access: Directly tap into the FDA drug labels dataset, with plans to incorporate the FDA adverse reactions dataset for a fuller data spectrum.
• Dynamic Retrieval: Utilize the Retrieval-Augmented Generation (RAG) technique for dynamic, real-time data retrieval.
• Intelligent Summaries: Leverage LLMs to generate insightful summaries and contextual answers.
• Interactive Learning: Engage with AI-generated related questions to deepen understanding and knowledge retention.
• Research Linkage: Automatically fetch and link relevant academic papers from PubMed, enhancing the depth of available information and supporting academic research.

Monitoring and Evaluation

• Real-Time Feedback with LangSmith: Use LangSmith to incorporate real-time feedback and custom evaluations. This system ensures that the AI's responses are not only accurate but also contextually aware and user-focused.
• Custom Evaluators for Enhanced Accuracy: Deploy custom evaluators like PharmAssistEvaluator to ensure responses meet high standards of relevance, safety, and perception as human-generated versus AI-generated.

How It Works

1. Query Input: Pharmacists type in their questions directly.
2. Data Retrieval: Relevant data is fetched from comprehensive datasets, including automated searches of PubMed for related academic papers.
3. Data Presentation: Data is displayed in an easily digestible format.
4. Summary Generation: Summaries of the data are created using GenAI
5. Question Suggestion: Suggest related questions to encourage further exploration.

Architecture

Hugging Face App Demo

Experience our app live on Hugging Face:

Home Screen

Demo Screen

LangSmith Performance Insights

Explore the effectiveness and interaction tracking of LangSmith in PharmAssistAI through these detailed screenshots:

Overview of Real-Time Evaluations

Detailed Feedback Example

Interaction Metrics Dashboard

Development Roadmap

• Integrate and index the complete FDA Drug Labeling and Adverse Events datasets.
• Refine the user interface for enhanced interaction and accessibility.
• Develop AI-driven educational tools like flashcards and study guides for mechanism of action.
• Enhance the retrieval system to include more open-source and advanced embedding models for better precision and efficiency.

Quick Start Guide

Simply enter your question about any FDA-approved drug in our chat interface, and PharmAssistAI will provide you with detailed information, summaries, and follow-up questions to help expand your research and understanding.

Feedback and Contributions

We value your input and invite you to help us enhance PharmAssistAI:

• 🐛 Report an issue on GitHub for technical issues or feature suggestions.
• 📧 Contact us at [email protected] for direct support or inquiries.