Article type
Abstract
Background: Systematic reviews are crucial in analyzing and evaluating available evidence on specific topics. Nevertheless, their effectiveness could be enhanced due to their time-consuming and resource-intensive characteristics, especially with the ever-increasing volume and complexity of the literature, resulting in challenges in identifying and filtering relevant papers and extracting vital information accurately. Artificial Intelligence (AI) presents a transformative opportunity for systematic reviews, promising substantial improvements across various process components.
Objective: This poster considers cutting-edge AI techniques and tools for systematic reviews, focusing on their efficiency, accuracy, and transparency implications. It emphasizes AI's potential to automate numerous stages of the review process.
Method: A survey of existing systematic reviews reveals that many tasks involved in systematic reviews are repetitive and thus amenable to AI automation. This includes detailing the tasks currently supported, the algorithms utilized, and available tools.
Results: More than 20 mostly free and accessible web-based programs are available to aid the systematic review process. These programs, particularly those utilizing natural language processing (NLP) and machine learning, can revolutionize systematic reviews by automating labor-intensive tasks such as study selection, data extraction, and quality assessment. Incorporating AI enables researchers to streamline the review process, mitigate human bias, and enhance result reproducibility. Moreover, AI-powered tools facilitate synthesizing and interpreting vast and heterogeneous data, enabling researchers to extract insights more efficiently.
Conclusion: Integrating AI into systematic reviews holds tremendous potential for advancing evidence synthesis in healthcare and other disciplines. Nonetheless, there are challenges and limitations to address, particularly regarding analyzing complex data and ensuring the reliability and transparency of AI-driven review processes. Ethical considerations surrounding data privacy, algorithmic bias, and the necessity for human oversight also require careful attention.
Objective: This poster considers cutting-edge AI techniques and tools for systematic reviews, focusing on their efficiency, accuracy, and transparency implications. It emphasizes AI's potential to automate numerous stages of the review process.
Method: A survey of existing systematic reviews reveals that many tasks involved in systematic reviews are repetitive and thus amenable to AI automation. This includes detailing the tasks currently supported, the algorithms utilized, and available tools.
Results: More than 20 mostly free and accessible web-based programs are available to aid the systematic review process. These programs, particularly those utilizing natural language processing (NLP) and machine learning, can revolutionize systematic reviews by automating labor-intensive tasks such as study selection, data extraction, and quality assessment. Incorporating AI enables researchers to streamline the review process, mitigate human bias, and enhance result reproducibility. Moreover, AI-powered tools facilitate synthesizing and interpreting vast and heterogeneous data, enabling researchers to extract insights more efficiently.
Conclusion: Integrating AI into systematic reviews holds tremendous potential for advancing evidence synthesis in healthcare and other disciplines. Nonetheless, there are challenges and limitations to address, particularly regarding analyzing complex data and ensuring the reliability and transparency of AI-driven review processes. Ethical considerations surrounding data privacy, algorithmic bias, and the necessity for human oversight also require careful attention.