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Using Explainable Artificial Intelligence in a Systematic Literature Review of Pressure Injury Prevention: Lessons

Joshua Morriss1,2,3,4, Laura E Edsberg1,2,3,4, Jill Cox1,2,3,4

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Journal of Wound, Ostomy, and Continence Nursing : Official Publication of the Wound, Ostomy and Continence Nurses Society
|March 4, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can assist with systematic literature reviews (SLR), but human expertise remains crucial. AI-generated reviews require thorough expert validation to ensure accuracy and clinical relevance in healthcare.

Keywords:
Explainable artificial intelligencePressure Injury preventionPressure injurySystematic literature review

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Area of Science:

  • Healthcare Informatics
  • Artificial Intelligence in Medicine
  • Clinical Research Methods

Background:

  • Artificial intelligence (AI) is increasingly utilized in healthcare to support clinical decisions and literature searches.
  • Systematic literature reviews (SLR) are essential for evidence-based practice but are time-consuming.
  • AI offers potential to streamline the SLR process.

Purpose of the Study:

  • To describe the methodology for developing an AI-generated SLR.
  • To identify lessons learned from a research team comprising content and AI experts.
  • To evaluate the accuracy and quality of an AI-generated SLR on pressure injury prevention.

Main Methods:

  • A proprietary explainable AI (XAI) platform using generative-discriminative algorithms and reinforcement learning was employed.
  • Content experts defined search parameters and exclusion criteria for the research question: 'What are best practices for pressure injury prevention in hospitalized patients?'
  • The XAI screened 1414 records following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines, with subsequent expert review.

Main Results:

  • The AI-generated SLR identified 110 studies, including 33 re-incorporated after expert review.
  • The AI-generated paper exhibited citation errors, misinterpretations, and mechanical narrative quality with factual inaccuracies.
  • Content expert review was critical for refining search terms, interpreting AI results, and ensuring clinical validity.

Conclusions:

  • Content experts are indispensable for accurate search term selection and result interpretation in AI-assisted SLRs.
  • Collaboration between content and AI experts is vital for optimizing AI applications in research.
  • Rigorous human review of AI-generated SLRs is essential to guarantee evidence fidelity and clinical applicability.