Editorial
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Methodol. Dec 20, 2023; 13(5): 384-389
Published online Dec 20, 2023. doi: 10.5662/wjm.v13.i5.384
New evidence-based practice: Artificial intelligence as a barrier breaker
Ricardo Maia Ferreira
Ricardo Maia Ferreira, Department of Sports and Exercise, Polytechnic Institute of Maia (N2i), Maia 4475-690, Porto, Portugal
Ricardo Maia Ferreira, Department of Physioterapy, Polytechnic Institute of Coimbra, Coimbra Health School, Coimbra 3046-854, Coimbra, Portugal
Ricardo Maia Ferreira, Department of Physioterapy, Polytechnic Institute of Castelo Branco, Dr. Lopes Dias Health School, Castelo Branco 6000-767, Castelo Branco, Portugal
Ricardo Maia Ferreira, Sport Physical Activity and Health Research & Innovation Center, Polytechnic Institute of Viana do Castelo, Melgaço, 4960-320, Viana do Castelo, Portugal
Author contributions: Ferreira RM designed the manuscript.
Conflict-of-interest statement: Ricardo Maia Ferreira has no conflit of interest to declare.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ricardo Maia Ferreira, PhD, Professor, Sports and Exercise Department (N2i), Polytechnic Institute of Maia, Avenida Carlos de Oliveira Campos, Maia 4475-690, Porto, Portugal. rferreira@ipmaia.pt
Received: October 17, 2023
Peer-review started: October 17, 2023
First decision: October 24, 2023
Revised: October 24, 2023
Accepted: November 8, 2023
Article in press: November 8, 2023
Published online: December 20, 2023
Abstract

The concept of evidence-based practice has persisted over several years and remains a cornerstone in clinical practice, representing the gold standard for optimal patient care. However, despite widespread recognition of its significance, practical application faces various challenges and barriers, including a lack of skills in interpreting studies, limited resources, time constraints, linguistic competencies, and more. Recently, we have witnessed the emergence of a groundbreaking technological revolution known as artificial intelligence. Although artificial intelligence has become increasingly integrated into our daily lives, some reluctance persists among certain segments of the public. This article explores the potential of artificial intelligence as a solution to some of the main barriers encountered in the application of evidence-based practice. It highlights how artificial intelligence can assist in staying updated with the latest evidence, enhancing clinical decision-making, addressing patient misinformation, and mitigating time constraints in clinical practice. The integration of artificial intelligence into evidence-based practice has the potential to revolutionize healthcare, leading to more precise diagnoses, personalized treatment plans, and improved doctor-patient interactions. This proposed synergy between evidence-based practice and artificial intelligence may necessitate adjustments to its core concept, heralding a new era in healthcare.

Keywords: Evidence, Clinicians, Patients, Artificial intelligence, Evidence-based practice

Core Tip: Evidence-based practice principles remain crucial in clinical care. However, practical application faces challenges. The recent emergence of artificial intelligence offers solutions for the main barriers. Artificial intelligence can swiftly provide evidence, enhances clinical decision-making, combat patient misinformation, and improve clinical consultations. The integration of artificial intelligence into evidence-based practice represents a potential paradigm shift, requiring some adjustments to the core concept of evidence-based practice.