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Systematic Reviews and other evidence synthesis projects

Introduction to Artificial Intelligence

How do we define Artificial Intelligence (AI)?

It is the theory and development of computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. 

Examples of AI: 

  • Automated synthesis of information
  • Natural language processing
  • Speech and text analysis 
  • Facial recognition

Purpose and Strategies

Purpose

  • Using AI as a mediating step in between sections of the systematic review process
  • Creates efficient operations and reduces the amount of time spent on more time-heavy portions
  • Using AI as an aid to make faster decisions
  • Increasing transparency and clarity in review questions

Strategies

  • Determine the strengths and weaknesses of different sections of the systematic review process
  • Identify the areas that take the most amount of time
  • Assess the risk in automation 
  • Talk to research and library team about where automated processes would benefit in the process

AI in Systematic Review Process

AI in Systematic Review Process

Steps of planning phase

Human Review Primary (in between first and second step):

  • AI can synthesize information to form a protocol
  • Checking to make sure elements of DEI are included in protocol and all components are presents

Steps of searching phase (literature)

Human Review Secondary (in between second and third step):

  • Autogenerated search strings
  • Automated literature selections; Conducting the quality check after return results 

Study review, selection, and information synthesis phase

Human Review Tertiary (in between third and fourth step):

  • Automated selection of studies; review selection criteria and process
  • Automated data extraction; review type of data and what is included and excluded
  • Automated synthesis of data; review for any biases and exclusions

Review composition phase

Benefits and Challenges

Machine Bias

  • Overestimations of research data input
  • Inaccurate or unfair predictions
  • Information exclusion
  • Overspecification 
  • Discrimination against specific groups

Research Bias

  • Lack of representation for marginalized groups in medical research 
  • Grey literature may not always be considered

Equity Considerations

  • May not consider equitable practices
  • With the presence of machine discrimination, equity may go out the window
  • Equity can be highlighted from the human lens
In an effort to strengthen the processes that use AI, it is important to provide feedback and speak up about any inconsistencies or biases noticed in the intermediate reviews. Also, always remember to assess the role of AI in your project and document when it was used in your methods section.

Resources

Guide Design Credit

Dev Wilder UW MLIS Candidate 2023