{
  "abstract": "Introduction Traditional data extraction strategies, such as human double extraction, are both time consuming and labour-intensive. Artificial intelligence (AI) has emerged as a promising tool for facilitating data extraction. However, it is not yet suitable as a standalone solution. We will conduct a randomised controlled trial (RCT) to compare the efficiency and accuracy of the AI-human data extraction strategy with human double extraction.Methods and analysis This study is designed as a randomised, controlled, parallel trial. Participants will be randomly assigned to either the AI group or the non-AI group at a 1:2 allocation ratio. The AI group will use a hybrid approach that combines AI extraction followed by human verification by the same participant, while the non-AI group will use human double extraction. Data will be collected for two tasks: event count and group size. Ten RCTs will be selected from an established database that analysed data extraction errors in systematic reviews of sleep medicine. The primary outcome measure will be the percentage of correct extractions by both groups for each data extraction task.Ethics and dissemination The trial is approved by the Ethics Council of Anhui Medical University (No. 81250507). We plan to publish the main results as an academic publication in an international peer-reviewed journal in 2026.Trial registration number Chinese Clinical Trial Register (Identifier: ChiCTR2500100393).",
  "authors": [
    {
      "affiliations": [
        "Key Laboratory of Population Health Across Life Cycle, Ministry of Education of the People's Republic of China, Anhui Medical University, Hefei, Anhui, China",
        "School of Public Health, Anhui Medical University, Hefei, Anhui, China"
      ],
      "name": "Zhen Peng"
    },
    {
      "affiliations": [
        "Proof of Concept Center, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital, Second Military Medical University, Naval Medical University, Shanghai, China"
      ],
      "name": "Shiqi Fan"
    },
    {
      "affiliations": [
        "Proof of Concept Center, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital, Second Military Medical University, Naval Medical University, Shanghai, China"
      ],
      "name": "Yuan Tian"
    },
    {
      "affiliations": [
        "School of Pharmaceutical Sciences, Anhui Medical University, Hefei, Anhui, China"
      ],
      "name": "Yingxia Wang"
    },
    {
      "affiliations": [
        "Clinical Research Institute, Institute of Advanced Clinical Medicine, Peking University, Beijing, China"
      ],
      "name": "Zongshi Qin"
    },
    {
      "affiliations": [
        "Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar"
      ],
      "name": "Suhail Doi"
    },
    {
      "affiliations": [
        "Key Laboratory of Population Health Across Life Cycle, Ministry of Education of the People's Republic of China, Anhui Medical University, Hefei, Anhui, China",
        "Proof of Concept Center, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital, Second Military Medical University, Naval Medical University, Shanghai, China",
        "School of Public Health, Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, Anhui, China"
      ],
      "name": "Chang Xu"
    }
  ],
  "title": "Comparing the accuracy of AI-assisted data extraction versus human double extraction in evidence synthesis: a randomised controlled trial protocol",
  "uid": "dbb3de6b-37f7-58ff-8656-3fb1926c6cf5"
}