Methodology for Autonomous Literature Reviews

1. System Architecture

• Overview of the DigitalKin autonomous literature review system

• Key components: data ingestion, processing, analysis, and output generation

2. Data Sources and Collection

• Academic databases used (e.g., PubMed, Web of Science, Google Scholar)

• Web scraping techniques for gathering additional relevant information

• Data preprocessing and cleaning methods

3. AI Algorithms and Models

• Natural Language Processing (NLP) techniques

  • Text classification

  • Named Entity Recognition (NER)

  • Sentiment analysis

• Machine Learning models for content relevance scoring

• Deep Learning approaches for understanding complex relationships in research

4. Review Process

• Query formulation and expansion

• Automated search and filtering

• Content extraction and summarization

• Citation network analysis

5. Output Generation

• Structured literature review format

• Visualization of key findings and relationships

• Bibliography and citation management

6. Evaluation Metrics

• Precision and recall of relevant papers

• Accuracy of content summarization

• Comparison with human-conducted reviews

• Time efficiency metrics

7. Continuous Learning and Improvement

• Feedback incorporation mechanism

• Model retraining and updating processes

8. Ethical Considerations

• Bias detection and mitigation strategies

• Privacy and data protection measures

• Transparency in AI decision-making process