Revised Surgical CAse REport (SCARE) 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 consider this advance.The SCARE Guideline 2025 update introduces a new AI-focused domain to promote transparency, reproducibility, and ethical integrity in surgical case reports (SCAREs) involving AI. METHODS A Delphi consensus exercise was conducted to update the SCARE guidelines. A panel of 49 surgical and scientific experts was 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 amongst 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 SCARE checklist is presented, which incorporates these new AI-related items. Authors are now expected to disclose AI involvement in patient care and manuscript preparation, as exemplified by this article. CONCLUSION The SCARE 2025 guideline provides an up-to-date framework for SCAREs in the era of AI. Through a robust consensus process, we have added specific reporting criteria for AI to ensure that any use of AI in a case report is documented, explained, and discussed concerning bias and ethics. This update will help maintain the quality, transparency, and clinical relevance of case reports, 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.100079
Citations
571
Primary Topic
Surgical Simulation and Training
Type
article
Field-Weighted Citation Impact
1145.67
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article

Revised Surgical CAse REport (SCARE) Guideline: An Update for the Age of Artificial Intelligence

Maliha Agha, Thomas Franchi, Ahmed Kerwan, Ahmed Al‐Jabir et al.
571 citations
Premier journal of science.
Surgical Simulation and Training
1145.67
article

Revised Surgical CAse REport (SCARE) 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
571 citations

Abstract

INTRODUCTION Artificial intelligence (AI) is rapidly transforming healthcare and scientific publishing. Reporting guidelines need to be updated to consider this advance.The SCARE Guideline 2025 update introduces a new AI-focused domain to promote transparency, reproducibility, and ethical integrity in surgical case reports (SCAREs) involving AI. METHODS A Delphi consensus exercise was conducted to update the SCARE guidelines. A panel of 49 surgical and scientific experts was 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 amongst 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 SCARE checklist is presented, which incorporates these new AI-related items. Authors are now expected to disclose AI involvement in patient care and manuscript preparation, as exemplified by this article. CONCLUSION The SCARE 2025 guideline provides an up-to-date framework for SCAREs in the era of AI. Through a robust consensus process, we have added specific reporting criteria for AI to ensure that any use of AI in a case report is documented, explained, and discussed concerning bias and ethics. This update will help maintain the quality, transparency, and clinical relevance of case reports, 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), Wellington Hospital (NZ), Imperial Valley College (US), Premier Research Group (US), University College London (GB), Imperial College London (GB)
Openalex Percentile: Top 0%
Surgical Simulation and Training
1145.67
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