The concept of artificial intelligence (AI) dates back to Turing’s seminal question “Can machines think?” in 1950.¹ The official birth of AI as a field is traced to the 1956 Dartmouth conference led by John McCarthy, which coined the term “artificial intelligence” and conjectured that aspects of learning and intelligence could be simulated by machines.² In the decades since, AI has transitioned from theory to real-world applications. Notably, AI has grown into a global industry valued at approximately $638 billion in 2024³ and projected to reach an economic impact of $15.7 trillion by 2030.⁴ This explosive growth (illustrated in Figure 1) is driven by breakthroughs in machine learning, big data, cloud computing and computational power.

In medicine and surgery, AI applications are increasingly prevalent. Early successes of medical AI have been seen in diagnostic specialties—for example, AI-driven image analysis in radiology and pathology has achieved impressive accuracy, often exceeding human performance in detecting subtle findings.⁵ In surgical disciplines, AI is being explored for enhancing preoperative planning, intraoperative guidance (such as robotic surgery and real-time decision support), and postoperative outcome prediction.⁵ These technologies promise to augment the surgeon’s capabilities and personalize patient care. However, with this promise comes new responsibility: clinicians and researchers must ensure that when AI is involved in patient management, it is reported transparently and with sufficient detail to appraise its validity and safety

Strengthening the Reporting of Cohort, Cross-Sectional and Case-Control Studies in Surgery (STROCSS) guideline—originally introduced in 2017⁶ and last updated in 2024⁷—required further revision to urgently address AI-related reporting.⁸ Developed by Agha et al., the original STROCSS checklist aimed to improve the clarity, consistency, and educational value of cohort studies in surgery.⁶ Subsequent updates in 2019,⁹ 2021,¹⁰ and 2024⁷ expanded and refined the criteria in response to feedback and evolving best practices. These guidelines have significantly improved reporting quality of surgical observational studies, although adherence by authors and journals has varied.¹¹ Prior research in this area has shown significant deficiencies in reporting among 92 case series that met inclusion criteria.¹² These included the following: failure to use standardized definitions (57%); missing or selective data (66%); transparency or incomplete reporting (70%); whether alternative study designs were considered (11%) and other issues (52%).¹²

Despite the growing presence of AI in healthcare, a gap exists in the STROCSS 2024 checklist—there were no specific items addressing how to report the use of AI in a surgical observational study. Omission of such details could lead to underreporting of critical information. In other study designs, the need for AI-specific reporting guidelines has been recognized; for instance, the CONSORT-AI and SPIRIT-AI extensions have been published to guide reporting of clinical trials and protocols involving AI.^8,13,14,15 To ensure that surgical observational studies keep pace with these developments, an update to the STROCSS guidelines was imperative. This 2025 update focuses on integrating AI-related reporting standards into the established STROCSS structure.

In this paper, we describe the methods and outcomes of the STROCSS 2025 guideline update. We introduce a new domain of checklist items dedicated to AI and elaborate on their rationale. We also discuss the importance of these additions in the context of transparency, bias mitigation, and reproducibility, which are crucial for maintaining trust in both surgical observational studies and AI systems. Notably, in alignment with the principle of transparency, we also document our own use of AI during the preparation of this manuscript, as recommended by emerging editorial policies.¹¹ The updated STROCSS 2025 guideline will help authors of surgical observational studies provide clear and accountable descriptions when AI is part of patient care or part of the report-generation. Ultimately, this will enhance the value of surgical observational studies as scholarly contributions in an era where AI is becoming an integral part of healthcare.

The STROCSS 2025 guideline represents a proactive evolution of surgical observational study standards in response to the growing influence of AI in healthcare. Compared to the STROCSS 2024 update, which primarily refined existing sections, the defining feature of STROCSS 2025 is the introduction of an entirely new domain dedicated to AI. This addition marks a significant broadening of the checklist’s scope—acknowledging that cohort, cross-sectional and case-control studies may now involve not only human clinicians and patients, but also AI tools as part of the diagnostic or therapeutic narrative. By explicitly addressing AI, the updated guidelines aim to enhance transparency and reproducibility. This aligns with broader efforts in medical research to improve reporting of AI.⁸

Transparency in reporting AI is the overarching theme of the new domain. Just as STROCSS champions transparency in clinical reporting, we recognize that AI algorithms must not become “black boxes” in case descriptions. Item 5a ensures that authors explicitly declare the use of AI, preventing scenarios where AI’s involvement might be obscured or assumed. This is analogous to disclosing a diagnostic test or a surgical device—readers deserve to know if AI was behind a key decision or outcome. Transparency is also reinforced by item 5b (tool identification) and 5c (development/data), which compel authors to provide enough technical detail for readers to grasp what the AI tool actually is. These items promote reproducibility: a future researcher or clinician reading the study should be able to identify the same AI tool, understand its training context, and thereby judge whether the case’s insights are transferable or credible in other settings.

The emphasis on bias mitigation and ethical considerations (item 5e and 5f) addresses increasing concerns about AI in medicine. AI systems, especially those based on machine learning, can inadvertently carry biases from their training data. If not reported, such biases could lead to misinterpretation of a case—for example, an AI diagnostic tool might perform poorly on certain demographic groups, which would be highly relevant if the study population belongs to that group. By asking authors to discuss AI biases and limitations, STROCSS 2025 aligns with the ethical principle of “do no harm” in publishing. It forces a moment of reflection: the case author must consider what AI might have missed or where it might be wrong. This practice can help mitigate overreliance on AI and encourages authors to validate AI outputs with clinical judgment. In a broader sense, it contributes to the literature on AI by documenting real-world challenges and failures, not just successes, thereby preventing publication bias in favor of positive AI results.

Future directions for STROCSS and AI in surgical observational studies may include further refinements as the technology evolves. We expect that as more cohort studies are published under the STROCSS 2025 guideline, a body of examples will accumulate, illustrating how authors have implemented these items. We will monitor the uptake of the AI domain—for instance, tracking if authors encounter difficulties in obtaining certain information about proprietary AI tools. If so, this might spur collaborations between clinicians and AI developers to improve transparency (e.g., requiring companies to provide model details when their AI is used in published cohort studies). Additionally, while our current items focus on AI in patient care, future updates might consider AI used in writing or reviewing studies. In fact, the academic community is actively discussing standards for disclosing AI assistance in manuscript preparation. In this STROCSS update, we touch on that aspect by encouraging disclosure (item 5f covers AI in manuscript if it involves patient data or content generation). It is plausible that a formal guideline for reporting the use of generative AI in scientific writing will emerge; until then, we have set a precedent by openly stating our use of an AI language model for editing this article.

It is worth reflecting on the limitations of our guideline update STROCSS. First, our Delphi panel, while diverse, was limited to 49 invitees with 46 responders. Important perspectives, such as patients or regulators, were not directly represented. Patients especially might have views on how they want AI usage reported in observational studies (perhaps desiring even more clarity on consent and privacy). In future guideline efforts, including patient representatives as well as other perspectives could be valuable. Second, the AI domain items are somewhat general and meant to apply across all types of AI. AI in surgery can range from simple diagnostic apps to complex autonomous robots; not every item will fit perfectly to every scenario. We attempted to strike a balance with broad wording, but there may be cases that require interpretation of how to apply an item. We will rely on the judgment of authors, reviewers, and editors to implement these guidelines sensibly on a case-by-case basis. Third, as with any consensus-based guideline, there is a degree of subjectivity in what was included and the language in which it is expressed. It is possible that some readers will feel an important AI-related item is missing. We welcome feedback from the surgical community, as the STROCSS guideline is meant to be iterative—future revisions (beyond 2025) can certainly expand or adjust the AI domain as needed.

One immediate challenge is dissemination and training. Introducing six new items means authors must be educated about them. We plan to disseminate the STROCSS 2025 checklist through the EQUATOR Network website, the Premier Science journals, and presentations at surgical conferences. Additionally, we will encourage journals to require STROCSS 2025 adherence in their observational study submissions, as endorsement by journals greatly drives usage. Experience from previous STROCSS iterations showed that when journal editors mandate the checklist and when authors see the benefit (in improved clarity of their reporting), compliance increases. We anticipate a similar positive impact: clearer reporting of studies involving AI, which in turn will make it easier for readers to learn from or even reproduce aspects of them (for instance, using the same AI tool on a similar patient). Ultimately, better reporting can feed into higher-level evidence; a well-documented case of AI successfully detecting a rare complication could spur larger studies or inspire others to utilize that AI tool.

During the preparation of this guideline manuscript, we made use of generative AI as a writing aid. Specifically, the tool was used in the later stages to assist with polishing language. No content generation (ideas or drafting of sections) was delegated to AI; it was employed similarly to a grammar/style assistant under close human oversight. We mention this to practice what we preach: transparency about AI usage. As journals and publishers, as well as COPE and WAME, increasingly require disclosure of AI assistance,^13,14 we demonstrate that such disclosure is feasible and can be done without undermining the credibility of the work. The final content was rigorously verified by all authors to eliminate any potential AI-introduced errors (such as incorrect references or “hallucinated” facts). We found that using AI in this limited capacity did improve efficiency in editing, but human expertise remained essential for the substance and accuracy of the guideline. This experience underscores a broader point: AI can be a valuable tool in medical writing and research, but it must be applied responsibly and transparently.

Through a structured Delphi consensus and in response to the rapid expansion of AI in healthcare, we have updated the STROCSS guideline to produce STROCSS 2025, a comprehensive reporting guideline for surgical observational studies in the age of AI. The addition of the new AI-focused domain (items 5a–5f) fills a critical gap, ensuring that any use of AI in a case is transparently reported with details on its implementation, validation, and ethical considerations. This update preserves the familiar structure of the STROCSS checklist while integrating modern considerations, thereby enabling authors to produce observational studies that are both up-to-date and rigorous. By following STROCSS 2025, clinicians and researchers will improve the clarity and reliability of observational studies, facilitating better knowledge sharing and ultimately enhancing patient care. As surgical practice increasingly intersects with advanced technologies, STROCSS 2025 will help maintain the integrity and educational value of observational studies, ensuring they remain a cornerstone of surgical literature in the years to come.