"Background: Systematic reviews highly labour-intensive to conduct due to the necessity of screening a large amount of articles, which results in a high consumption of research resources. Artificial intelligence (AI) is seen as a potential solution to this problem. AI provides several efficient and innovative options in this process. AI is a broad set of technologies and methods, focusing on automating reasoning tasks such as planning, understanding, predicting, and classifying. Machine learning is the sub-discipline of AI that focuses on developing ways for AI systems to learn from experience. AI offers the possibility of automation and decision support for skilled tasks.
Objective: To evaluate the role of Artificial Intelligence in increasing the efficiency in terms of timelines and quality of systematic reviews
Methodology: We propose to evaluate the role of artificial intelligence tools in increasing the efficiency and reducing the time taken in conduct of different steps of systematic reviews. Use of the the following automation tools is proposed in the conduct of systematic review. A Search refiner tool will be used to decide on the keywords & databases during search strategy. SRA De-duplication will be used to de-duplicate and merge identical citations after the searches are complete. SRA helper will be used during the screening of articles to exclude irrelevant trials and Robot Reviewer will be used to assess the risk of bias in included studies.
Results: Time taken in the conduct of systematic review using the above tools will be compared to the normal timelines of a typical systematic review. Challenges in applying AI in terms of data availability and quality will be compared and presented.
Conclusion: Results would be helpful in justifying the role of artificial intelligence tools in decreasing the timeline of systematic reviews. It would be helpful in mainstreaming the AI tools in evidence production

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