Revised Strengthening the Reporting of Cohort, Cross-Sectional and Case-Control Studies in Surgery (STROCSS) Guideline: An Update for the Age of Artificial Intelligence

INTRODUCTION Artificial intelligence (AI) is rapidly transforming healthcare and scientific publishing. Reporting guidelines need to be updated to take into account this advance. The STROCSS Guideline 2025 update adds a new AI-focused domain to promote transparency, reproducibility, and ethical integrity in surgical observational studies involving AI. METHODS A Delphi consensus exercise was conducted to update the STROCSS guidelines. A panel of 49 surgical and scientific experts were invited to rate proposed new items. In Round 1, participants scored each item on a nine-point Likert scale and provided feedback. Items not meeting consensus were revised or discarded. RESULTS A 94% response rate occurred among participants (46/49) in the first round. Ratings were analyzed for agreement levels, and consensus was reached on all six proposed AI-related items. A revised STROCSS checklist is presented which incorporates these new AI-related items. Authors are now expected to disclose AI involvement not only in patient care but also in manuscript preparation, as exemplified by this article. CONCLUSION The STROCSS 2025 guideline provides an up-to-date framework for surgical observational studies in the era of AI. Through a robust consensus STROCSS, we have added specific reporting criteria for AI to ensure that any use of artificial intelligence in a surgical observational study is clearly documented, explained, and discussed including with respect to bias and ethics. This update will help maintain the quality, transparency, and clinical relevance of surgical observational studies, ultimately improving their educational value and trustworthiness for the surgical community.

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Publication Details

Journal
Premier journal of science.
Published
2025-05-22
DOI
https://doi.org/10.70389/pjs.100081
Citations
29
Primary Topic
Cardiac, Anesthesia and Surgical Outcomes
Type
article
Field-Weighted Citation Impact
40.26
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article

Revised Strengthening the Reporting of Cohort, Cross-Sectional and Case-Control Studies in Surgery (STROCSS) Guideline: An Update for the Age of Artificial Intelligence

Maliha Agha, Thomas Franchi, Ahmed Kerwan, Ahmed Al‐Jabir et al.
29 citations
Premier journal of science.
Cardiac, Anesthesia and Surgical Outcomes
40.26
article

Revised Strengthening the Reporting of Cohort, Cross-Sectional and Case-Control Studies in Surgery (STROCSS) Guideline: An Update for the Age of Artificial Intelligence

Maliha Agha, Thomas Franchi, Ahmed Kerwan, Ahmed Al‐Jabir, Maria Nicola, Catrin Sohrabi, Ginimol Mathew, Riaz Agha, Rasha Rashid
article en
29 citations

Abstract

INTRODUCTION Artificial intelligence (AI) is rapidly transforming healthcare and scientific publishing. Reporting guidelines need to be updated to take into account this advance. The STROCSS Guideline 2025 update adds a new AI-focused domain to promote transparency, reproducibility, and ethical integrity in surgical observational studies involving AI. METHODS A Delphi consensus exercise was conducted to update the STROCSS guidelines. A panel of 49 surgical and scientific experts were invited to rate proposed new items. In Round 1, participants scored each item on a nine-point Likert scale and provided feedback. Items not meeting consensus were revised or discarded. RESULTS A 94% response rate occurred among participants (46/49) in the first round. Ratings were analyzed for agreement levels, and consensus was reached on all six proposed AI-related items. A revised STROCSS checklist is presented which incorporates these new AI-related items. Authors are now expected to disclose AI involvement not only in patient care but also in manuscript preparation, as exemplified by this article. CONCLUSION The STROCSS 2025 guideline provides an up-to-date framework for surgical observational studies in the era of AI. Through a robust consensus STROCSS, we have added specific reporting criteria for AI to ensure that any use of artificial intelligence in a surgical observational study is clearly documented, explained, and discussed including with respect to bias and ethics. This update will help maintain the quality, transparency, and clinical relevance of surgical observational studies, ultimately improving their educational value and trustworthiness for the surgical community.

Premier journal of science.
University College Hospital (GB), Harvard University (US), Royal Free London NHS Foundation Trust (GB), Imperial Valley College (US), Hutt Hospital (NZ), Premier Research Group (US), University College London (GB), Imperial College London (GB)
Openalex Percentile: Top 0%
Cardiac, Anesthesia and Surgical Outcomes
40.26
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