About ReviewAid.

ReviewAid is an open-source AI-driven tool designed to streamline full-text screening and data extraction phases of systematic reviews. It leverages advanced large language models to classify papers based on PICO criteria and extract custom data fields, drastically reducing manual workload for researchers.

Want more details / documentation about ReviewAid? Please visit aurumz-rgb.github.io/ReviewAid/ or Github Repository.

ReviewAid Full text Screener
ReviewAid Data Extractor
GitHub Repository Launch ReviewAid

If the primary ReviewAid system is experiencing high usage, resource saturation, or memory limitations, users may utilize available mirror versions of ReviewAid without restriction.

ReviewAid Mirror
Note: ReviewAid is not intended to replace manual screening and data extraction. Rather, it is designed to function as an independent supplementary reviewer, helping to minimize human error and enhance the overall precision, consistency, and reliability of the research process.
IMP: Please restrict each submission to a maximum of 20 articles. Submissions exceeding this limit will result in processing only the first 20 articles, after which the process will terminate prematurely. Kindly adhere to this restriction. Please respect this limit.

Key Features

1. Full-text PICO Screening

AI-based PICO specified inclusion/exclusion classification

2. Full-text Data Extraction

Custom field extraction from text

3. Batch Processing

Process up to 20 papers at once

4. Multiple Exports

Export CSV, Excel, and Word formats

5. Live Terminal

Real-time processing logs to ensure transparency

6. Confidence Scoring

Estimates reliability of extraction to guide researchers to trust/not trust

7. Configuration

Configure any AI model using API key

8. Use

Runs locally/online, highly reusable

9. Open-source

Made by Researchers to ensure no proprietary "black box"

You can also check out the full walkthrough and demonstration of ReviewAid on YouTube

Confidence Scoring System

This layered approach ensures that high-confidence decisions are automated safely, while ambiguous or unreliable cases are clearly flagged for human oversight.

Confidence Score Classification Description Implication
1.0 (100%) Definitive Match Deterministic rule-based classification / No ambiguity. Fully automated decision
0.8 – 1.0 Very High AI strongly validates the decision using explicit textual evidence. Safe to accept
0.6 – 0.79 High Criteria appear satisfied based on standard academic structure and content. Review optional
0.4 – 0.59 Moderate Ambiguous context or loosely met criteria. Manual verification recommended
0.1 – 0.39 Low Based mainly on heuristic keyword estimation. High risk of error
< 0.1 Unreliable Derived from fallback or failed extraction methods. Mandatory manual review

Usage & Installation

Follow these instructions to run ReviewAid online or locally.

⚡ Usage (Online)

  1. Launch Online Streamlit hosted web app
    Access the application directly from your browser without installation.
  2. Select Mode:
    • Full-text Paper Screener: Choose this mode to screen papers based on PICO (Population, Intervention, Comparison, Outcome) criteria.
    • Full-text Data Extractor: Choose this mode to extract specific fields (Author, Year, Conclusion, etc.) from research papers.
  3. Workflow (Screener):
    • Enter your PICO criteria (Inclusion/Exclusion) in the input fields.
    • Upload your PDF papers (Batch upload supported).
    • Click "Screen Papers".
    • Monitor the "System Terminal" for real-time logs of extraction, API calls, and processing status.
    • View the "Screening Dashboard" for a pie chart of Included/Excluded/Maybe decisions.
    • Download results as CSV, XLSX, or DOCX.
  4. Workflow (Extractor):
    • Enter the fields you want to extract (comma-separated).
    • Upload your PDF papers.
    • Click "Process Papers".
    • Monitor the "System Terminal" for logs.
    • View extracted data in the dashboard.
    • Download extracted data as CSV, XLSX, or DOCX.
  5. Configuration:
    • For using API key, you can select the respective AI model in either Screener/Extractor.

⚡ Usage (run streamlit Locally)

To run ReviewAid locally with your own API keys (OpenAI, DeepSeek, etc.), follow these steps:

  1. Clone the repository
    git clone https://github.com/aurumz-rgb/ReviewAid.git
    cd ReviewAid
  2. Create and activate a virtual environment (recommended)
    python -m venv venv
    source venv/bin/activate   # macOS / Linux
    venv\Scripts\activate   # Windows
  3. Install dependencies
    pip install -r requirements.txt
  4. Start the Streamlit application
    streamlit run app.py
  5. Configure AI model along with API key inside the UI
    • Select AI model as the provider
    • Enter your API Key

🖥️ Running ReviewAid Locally with Ollama (No API Key Required)

ReviewAid supports local inference using Ollama, allowing you to run the application without any external API keys. This is ideal for users who prefer offline usage, enhanced privacy, or full local control.

Prerequisites

Ensure the following are installed on your system:

  • Python 3.12+
  • Ollama (installed and running locally)
  • At least one supported Ollama model (e.g., llama3)

Pull a model (example):

ollama pull llama3

Verify Ollama is running:

ollama list

▶️ Running ReviewAid with Ollama

  1. Clone the repository
    git clone https://github.com/aurumz-rgb/ReviewAid.git
    cd ReviewAid
  2. Create and activate a virtual environment (recommended)
    python -m venv venv
    source venv/bin/activate   # macOS / Linux
    venv\Scripts\activate   # Windows
  3. Install dependencies
    pip install -r requirements.txt
  4. Start the Streamlit application
    streamlit run app.py
  5. Configure Ollama inside the UI
    • Select Ollama (Local) as the provider
    • Choose a local model (e.g., llama3)
    • No API key is required

Privacy Advantage

When using Ollama:

  • All inference runs entirely on your local machine
  • No data is sent to external servers
  • No API keys are required or stored

This makes Ollama the most privacy-preserving configuration supported by ReviewAid.

Notes
  • Performance depends on your local hardware (CPU/GPU/RAM)
  • Large PDFs or batch sizes may take longer on CPU-only systems
  • For best results, ensure Ollama is running before launching Streamlit

Configuration

OpenAI Logo Anthropic Logo DeepSeek Logo Cohere Logo GLMV Logo Ollama Logo

ReviewAid also supports configuration of OpenAI, Claude, Deepseek, Cohere, Z.ai and Ollama (locally) via API key as well. To protect your privacy, API keys are not stored at any time.

For tested tasks, the following models were successful:

OpenAI – GPT-4o

Deepseek – deepseek-chat

Cohere – command-a-03-2025

Z.AI – GLM-4.6V-Flash, GLM-4.5V-Flash

Anthropic – Claude-Sonnet-4-20250514

Ollama (local) – Llama3

Default – GLM-4.6V-Flash

Acknowledgements

ZAI GLM-4.6V-Flash Logo

I gratefully acknowledge developers of GLM-4.6V-Flash (Z.ai) for providing the AI model used in ReviewAid.

The visual and text-based reasoning capabilities of GLM-4.6V-Flash have greatly enhanced ReviewAid's full-text screening and data extraction workflows.

For more information, please see GLM-4.6V-Flash paper and GLM-4.6V-Flash Hugging Face.

I would also like to thank Mohith Balakrishnan for his thorough validation of ReviewAid, including batch testing, error checks, and confidence verification, which significantly improved the tool’s reliability and accuracy.

Citation

For ReviewAid's preprint paper, please check ReviewAid MetaArXiV.

If you use ReviewAid in your research, please cite it using the following format: