The document discusses the process and significance of data extraction in systematic reviews and meta-analyses, highlighting challenges such as time consumption and manual errors. It introduces AI-powered tools as solutions to enhance efficiency and accuracy in data extraction, stressing the importance of integrating AI with human expertise. Best practices for ensuring data quality and combining AI with traditional methods are also presented.

1. Data
Extraction: AI
tools for
performing
data extraction

2. Purpose of Talk
UNDERSTANDING
DATA EXTRACTION IN
SYSTEMATIC REVIEWS
AND META-ANALYSIS
HIGHLIGHTING
CHALLENGES AND
SOLUTIONS
INTRODUCING AI-
POWERED TOOLS
SHARING BEST
PRACTICES.
LIVE DEMO Q&A

3. Purpose of Talk
UNDERSTANDING
DATA EXTRACTION IN
SYSTEMATIC REVIEWS
AND META-ANALYSIS
HIGHLIGHTING
CHALLENGES AND
SOLUTIONS
INTRODUCING AI-
POWERED TOOLS
SHARING BEST
PRACTICES.
LIVE DEMO Q&A

4. Definition of
Systematic Reviews
A systematic review is a
research method that
involves systematically
collecting, critically
analyzing, and synthesizing
all relevant studies on a
particular topic. The goal is
to provide a comprehensive
and unbiased summary of
the existing evidence.

5. Definition of
Meta-Analysis
A meta-analysis is a
statistical technique used to
combine the results of
multiple studies to arrive at
a single conclusion with
greater statistical power. It
often accompanies a
systematic review and helps
to identify patterns,
strengths, and weaknesses
in the research.

6. Importance and Benefits
Evidence-Based
Decision
Making
Reducing Bias
and Increasing
Reliability
Identifying
Research Gaps

7. Key Steps in Conducting a
Systematic Review
FORMULATING
A RESEARCH
QUESTION
DEVELOPING A
PROTOCOL
LITERATURE
SEARCH
SCREENING AND
SELECTION
DATA
EXTRACTION
QUALITY
ASSESSMENT
DATA SYNTHESIS
INTERPRETING
AND REPORTING
RESULTS

8. www.knust.edu.gh
www.knust.edu.gh

9. www.knust.edu.gh
www.knust.edu.gh

10. www.knust.edu.gh
www.knust.edu.gh

11. Challenges in Conducting Systematic
Reviews and Meta-Analysis
• Time-consuming
and Resource-
Intensive
• Publication Bias and
Data Quality
• Complexity in Data
Synthesis

12. Purpose of Talk
UNDERSTANDING
DATA EXTRACTION IN
SYSTEMATIC REVIEWS
AND META-ANALYSIS
HIGHLIGHTING
CHALLENGES AND
SOLUTIONS
INTRODUCING AI-
POWERED TOOLS
SHARING BEST
PRACTICES.
LIVE DEMO Q&A

13. Key Steps in Conducting a Systematic
Review
FORMULATING
A RESEARCH
QUESTION
DEVELOPING A
PROTOCOL
LITERATURE
SEARCH
SCREENING AND
SELECTION
DATA
EXTRACTION
QUALITY
ASSESSMENT
DATA SYNTHESIS
INTERPRETING
AND REPORTING
RESULTS

14. Data extraction is the process
of systematically collecting
relevant data from included
studies in a systematic review
or meta-analysis. This step is
crucial because the accuracy
and consistency of the
extracted data directly
impact the quality and
validity of the review's
findings.
Definition and
Importance

15. Challenges in Manual Data
Extraction
Time-
Consuming
and Labor-
Intensive
Prone to
Human Error
Inconsistency
and Variability
Subjectivity
and Bias
Dealing with
Different Study
Designs
Extracting
Complex
Outcome
Measures

16. Impact of
These
Challenges
Threats to Data
Quality
Delays in Review
Completion
Resource Intensity

17. Strategies
to Mitigate
Challenges STANDARDIZED DATA
EXTRACTION FORMS
TRAINING AND
CALIBRATION OF
REVIEWERS
DOUBLE DATA
EXTRACTION AND
CONSENSUS
USE OF DATA
EXTRACTION SOFTWARE
E.G. AI TOOLS

18. Purpose of Talk
UNDERSTANDING
DATA EXTRACTION IN
SYSTEMATIC REVIEWS
AND META-ANALYSIS
HIGHLIGHTING
CHALLENGES AND
SOLUTIONS
INTRODUCING AI-
POWERED TOOLS
SHARING BEST
PRACTICES.
LIVE DEMO Q&A

19. What is Artificial
Intelligence (AI)
Artificial Intelligence (AI) refers to the
simulation of human intelligence in
machines that are programmed to think
and learn like humans. These systems can
perform tasks such as recognizing patterns,
making decisions, and predicting outcomes.

20. Role of AI in
Data Extraction
• Automating
Repetitive Tasks
• Improving Accuracy
and Consistency
• Handling Large
Volumes of Data

21. Benefits of Using
AI in Data
Extraction
• Efficiency and Speed
• Scalability
• Enhanced Accuracy
• Cost-Effectiveness

22. Challenges and
Limitations of AI
in Data
Extraction
• Initial Setup and
Training
• Quality of Training
Data
• Interpretability and
Transparency
• Ongoing
Maintenance and
Updates

23. Examples of AI Applications
in Data Extraction
https://www.rayyan.ai/

24. Examples of AI Applications
in Data Extraction
https://www.covidence.org/

25. Examples of AI Applications
in Data Extraction
https://www.covidence.org/

26. Examples of AI Applications
in Data Extraction
https://asreview.nl/

27. Examples of AI Applications
in Data Extraction
https://typeset.io/

28. Purpose of Talk
UNDERSTANDING
DATA EXTRACTION IN
SYSTEMATIC REVIEWS
AND META-ANALYSIS
HIGHLIGHTING
CHALLENGES AND
SOLUTIONS
INTRODUCING AI-
POWERED TOOLS
SHARING BEST
PRACTICES.
LIVE DEMO Q&A

29. Best Practices and Tips
1. Integrating AI with Human
Expertise
✓Complementary Roles
✓Training and Calibration

30. Best Practices and Tips
2. Ensuring Data Quality and
Accuracy.
✓Validation of AI Outputs.
✓Quality Control Checks.

31. Best Practices and Tips
3. Combining AI Tools with
Traditional Methods.
✓Hybrid Approach.
✓Sequential Workflow.

32. Purpose of Talk
UNDERSTANDING
DATA EXTRACTION IN
SYSTEMATIC REVIEWS
AND META-ANALYSIS
HIGHLIGHTING
CHALLENGES AND
SOLUTIONS
INTRODUCING AI-
POWERED TOOLS
SHARING BEST
PRACTICES.
LIVE DEMO Q&A

34. Purpose of Talk
UNDERSTANDING
DATA EXTRACTION IN
SYSTEMATIC REVIEWS
AND META-ANALYSIS
HIGHLIGHTING
CHALLENGES AND
SOLUTIONS
INTRODUCING AI-
POWERED TOOLS
SHARING BEST
PRACTICES.
LIVE DEMO Q&A

35. CONTACT
Cyril D. Boateng (PhD)
Linkedin
+233559580392
cyrilboat@knust.edu.gh

36. Kwame Nkrumah University of
Science & Technology, Kumasi, Ghana
37
THANK YOU