How to Do Full-Text Reviews and Evidence Synthesis (With AI) скачать в хорошем качестве

How to Do Full-Text Reviews and Evidence Synthesis (With AI)

Full-text reviews are where literature reviews get super slow. Large systematic reviews can take 16+ months - and full-text review is the biggest chunk of that time. In this session, I break down what full-text reviews actually involve (screening, data extraction, risk of bias), where AI can meaningfully reduce workload, and how evidence synthesis fits into the process. I also cover limitations and best practices for keeping humans in the loop. This is part 3 of a 4-part series: AI Across the Research Stack. TIMESTAMPS: 0:00 The Research Funnel (Recap) 1:55 Screening vs Full-Text Review 3:05 Three Components of Full-Text Review 4:28 What Full-Text Screening Looks Like (Rayyan) 5:16 Data Extraction Example (Covidence) 6:11 Risk of Bias Assessments 7:13 Main Issues with Manual Full-Text Review 8:28 Best AI Use Cases for Full-Text 10:16 Annotations in moara.io 12:11 Time Savings Potential (99.6%?) 13:13 What Is Evidence Synthesis? 15:02 Evidence Synthesis Output Example 16:19 AI for Qualitative vs Quantitative Synthesis 19:14 Evidence Synthesis in moara.io 19:55 Limitations and Best Practices 22:05 Wrap-Up Key takeaway: AI is best at automating data extraction and cataloging — not replacing the analysis. Keep humans in the loop. Part 1 (Search Strategy):    • Why Literature Search Strategies Should Be...   Part 2 (Screening):    • Why Most Literature Reviews Skip Screening...   Try moara.io: https://moara.io