The Literature Review Network
An Explainable Artificial Intelligence for Systematic Literature Reviews, Meta-analyses, and Method Development
Methods Study and Systematic Review
Systematic literature reviews are the highest quality of evidence in research. However, the review process is hindered by significant resource and data constraints. The Literature Review Network (LRN) is the first of its kind explainable AI platform adhering to PRISMA 2020 standards, designed to automate the entire literature review process.
Mouldable technology in ostomy care: a scoping review of the literature using a novel, explainable artificial intelligence
Scoping Review
Using LRN by Ziplitics, this paper was analyzed to highlight key insights from a global expert panel that evaluated moldable ostomy technologies using a modified Delphi consensus process. The review compares moldable baseplates with traditional cut-to-fit appliances, emphasizing the importance of appropriate product selection, education, and access to specialized ostomy care in reducing peristomal complications. It also presents six evidence-based recommendations to support clinical decision-making and improve outcomes for people living with an ostomy.
Use of mouldable ostomy technology in clinical practice: Delphi clinical consensus of ostomy experts
Delphi Consensus Statement
Supported by LRN by Ziplitics, this study reviews the current evidence and expert perspectives on moldable ostomy technologies through a modified Delphi consensus process. The research compares moldable stoma baseplates with traditional cut-to-fit appliances and provides six consensus statements to guide clinical practice. The findings emphasize evidence-based product selection and strategies to improve ostomy care and reduce peristomal complications.
Introduction to Artificial Intelligence for the Wound, Ostomy, and Continence Nurse
Professional Practice Article
Analyzed with LRN by Ziplitics, this article explores how artificial intelligence is reshaping wound, ostomy, and continence (WOC) nursing by supporting clinical decision-making, diagnosis, prognosis, and treatment. It examines the evolution of AI, explains key concepts, and addresses the challenges of "black box" AI systems that lack transparency. The paper emphasizes the importance of explainable AI and highlights the need for WOC nurses to understand AI technologies so they can be applied ethically, responsibly, and in partnership with clinical expertise to improve patient care.
Using Explainable Artificial Intelligence in a Systematic Literature Review of Pressure Injury Prevention: Lessons Learned
Methods Study
Generated with LRN by Ziplitics, this study examines how explainable AI can support the development of a systematic literature review on best practices for pressure injury prevention in hospitalized patients.
Mining Academic Pharmacy Mentorship Literature Gold With Explainable AI
Abstract, Methods Study
Leveraging LRN by Ziplitics, this study evaluates the effectiveness of an explainable Artificial Intelligence (AI) platform for conducting a literature search on mentorship in academic pharmacy. The findings explore how explainable AI can streamline the literature search process, improve the identification of relevant research, and support more efficient and transparent evidence discovery in academic and healthcare settings.
From Manual to Machine Revolutionizing Literature Searches for Mentoring
Abstract, Methods Study
Powered by LRN by Ziplitics, this study compares literature review results on mentorship in healthcare disciplines generated by an Explainable Artificial Intelligence (XAI) platform with traditional manual searches conducted by pharmacy students and faculty. The findings evaluate how AI-assisted literature reviews perform in identifying relevant evidence, demonstrating the potential of explainable AI to enhance the speed, consistency, and efficiency of evidence discovery while maintaining transparency throughout the review process.
Ziplitics
