{
  "abstract": "Objectives Systematic reviews (SR) play a crucial role in synthesizing scientific literature for evidence-based decision-making, but traditional methods remain time-consuming and prone to inconsistencies. To enhance efficiency, we evaluated the feasibility of using GPT-based automated search techniques for retrieving occupational cancer-related studies from the PUBMED database.Material and Methods The assessment was conducted across seven neoplastic sites (i.e. nasopharynx, lymphomas, bladder, larynx, ovary, breast and multiple myeloma) each involving a review of 100 articles using an AI-generated search prompt. The evaluation focused on title and abstract screening, leveraging automated classification to identify relevant studies on occupational exposures and cancer risks. Only case-control, cohort, cross-sectional studies, and meta-analyses indicating an association between occupational sector and neoplasm were included in the selection.Results The data extracted by GPT were subsequently reviewed by a human gold standard expert in occupational epidemiology. Compared to expert classification, GPT missed 5 out of 164 relevant studies (false negative rate: 3.0%) and flagged 69 irrelevant ones as relevant out of 536 true negatives (false positive rate: 11.4%), demonstrating the potential of AI-assisted literature screening in streamlining systematic reviews.Conclusions The findings suggest that GPT-based automated search techniques can effectively support the initial phases of systematic reviews by efficiently identifying relevant occupational cancer studies with a relatively low rate of false negatives. These results support the feasibility of using GPT-based tools for first-pass screening in occupational cancer SRs. While further refinement is needed to reduce false positives, this approach offers a promising balance between speed and sensitivity, with potential for broader integration into systematic review workflows",
  "authors": [
    {
      "affiliations": [
        "Occupational Medicine Unit – Department of Public Health, Experimental and Forensic Medicine University of Pavia, Pavia, Italy. Hospital Occupational Medicine Unit, ICS Maugeri IRCCS, Pavia, Italy. Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy"
      ],
      "name": "Luca D’Amato"
    },
    {
      "affiliations": [
        "Occupational Medicine Unit – Department of Public Health, Experimental and Forensic Medicine University of Pavia, Pavia, Italy. Hospital Occupational Medicine Unit, ICS Maugeri IRCCS, Pavia, Italy. Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy"
      ],
      "name": "Roberta Pernetti"
    },
    {
      "affiliations": [
        "Occupational Medicine Unit – Department of Public Health, Experimental and Forensic Medicine University of Pavia, Pavia, Italy. Hospital Occupational Medicine Unit, ICS Maugeri IRCCS, Pavia, Italy. Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy"
      ],
      "name": "Giorgia Stoppa"
    },
    {
      "affiliations": [
        "Occupational Medicine Unit – Department of Public Health, Experimental and Forensic Medicine University of Pavia, Pavia, Italy. Hospital Occupational Medicine Unit, ICS Maugeri IRCCS, Pavia, Italy. Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy"
      ],
      "name": "Corrado Lanera"
    },
    {
      "affiliations": [
        "Occupational Medicine Unit – Department of Public Health, Experimental and Forensic Medicine University of Pavia, Pavia, Italy. Hospital Occupational Medicine Unit, ICS Maugeri IRCCS, Pavia, Italy. Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy"
      ],
      "name": "Dolores Catelan"
    },
    {
      "affiliations": [
        "Occupational Medicine Unit – Department of Public Health, Experimental and Forensic Medicine University of Pavia, Pavia, Italy. Hospital Occupational Medicine Unit, ICS Maugeri IRCCS, Pavia, Italy. Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy"
      ],
      "name": "Enrico Oddone"
    }
  ],
  "title": "8282215 Evaluating GPT-based automated search for occupational cancer studies: enhancing efficiency in systematic reviews",
  "uid": "d6c60af8-9a80-5720-978a-4ee9674e0cdb"
}