Potential of Artificial Intelligence in Evidence-Based Practice in Nursing - Revista Brasileira de Enfermagem
Revista Brasileira de Enfermagem. 09-09-2024;77(5):e770501
Evidence-based practice (EBP) has established itself as a fundamental pillar in nursing, driving effective clinical decision-making based on high-quality 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.
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Evidence-based practice (EBP) has established itself as a fundamental pillar in nursing, driving effective clinical decision-making based on high-quality 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.
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