Potential of Artificial Intelligence in Evidence-Based Practice in Nursing.

Abstract

of Evidence-based practice (EBP) has established itself as a fundamental pillar in nursing, driving effective clinical decision-making based on highquality scientific research.The primary goal of EBP is to ensure that patients receive the most appropriate and safe care, based on the best available evidence.In this context, knowledge synthesis methods are essential tools for EBP, as they contribute to clinical decision-making based on robust reviews, resulting from the combination of several studies in the more than 28,000 scientific articles published in health annually.However, the current scientific panorama is characterized by a massive production of knowledge, making the task of synthesizing and interpreting evidence a Herculean challenge for healthcare professionals.With these challenges in mind, artificial intelligence (AI) emerges as a powerful tool, capable of revolutionizing EBP and making it more efficient and accurate, advancing the time and quality of research.AI, with its ramifications in machine learning (ML) and natural language processing (NLP), provides techniques capable of processing and analyzing colossal volumes of data, including the vast scientific literature.Some AI tools, such as Elicit, Consensus, Litmaps, Perplexity, Semantic Scholar, ResearchRabbit, Paper Digest, Scholarcy and Open Knowledge Maps, have already mapped more than 280,000 scientific articles and, based on ML, promise to revolutionize the identification of knowledge gaps.Imagine AI combing through thousands of articles, revealing unexplored areas and outlining new frontiers for nursing research, freeing up precious time for researchers to dedicate themselves to robust investigations.With this evolution, literature mapping and relevance, such as listing articles and elaborating research problems, can be discussed in real time between researchers.Another application is in the selection of relevant articles for research, automating the screening process and prioritizing the reading of articles with the highest probability of relevance.The ASReview platform, for instance, uses ML techniques to automate article screening, prioritizing the reading of those most likely to be relevant.This functionality speeds up the selection of studies that will make up part of the review, allowing researchers to focus on the critical analysis of the most promising studies and also highlight the most important sections of the studies in an exploratory stage.AI also emerges as an ally in the precise delimitation of study populations, which is extremely important to guarantee the validity and applicability of research results.Tools such as Litbaskets allow analyzing the representativeness of journals in relation to a specific topic, guiding researchers in choosing relevant data sources.Furthermore, AI can help optimize research protocols, improving experimental design and minimizing time and costs.Consensus, for instance, indicates studies that confirm or refute possible practices and also categorizes articles based on the methodological approach, facilitating the systematization of knowledge through different methodological approaches.Data collection, a traditionally laborious step in review research, also

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