From calculators to artificial intelligence: moving beyond rejection to responsible adoption.

Academia, accountability, artificial intelligence, authorship, evidence-based care Sergio Haimovich Reproductive Surgery Unit, Embriogyn Clinic, Tarragona, Spain Department of Obstetrics and Gynecology, Republic University Faculty of Medicine, Montevideo, Uruguay Board of Directors, American Association of Gynecologic Laparoscopists (AAGL), California, United States of America From calculators to artificial intelligence: moving beyond rejection to responsible adoption DOI: 10.52054/FVVO.2025.276 Haimovich et al. From calculators to AI: toward responsible adoption Independent “clinically meaningful” research questions are not driven by AI, at least for now. Conceptual originality, ethical design, and accountability remain human if and when Artificial General Intelligence, a general-purpose system with human-level or greater competence across domains, able to learn, reason, plan, transfer knowledge across tasks, and act autonomously, arrives, we will need to revisit boundaries. That future debate should not paralyse today’s progress. In the meantime, the stance of academia should switch from “ban or detect” to the affirmative “govern and leverage with safeguards”. We can be guided by a set of basic principles for adopting in a responsible way:3 • Transparency: Reveal AI use (what tools, when, with what controls). • Authorship and Responsibility: Humans are solely responsible; AI is not a co-author. • Data Integrity: No artificial data without specifying it as such, no reinvention of data inside images/figures; control over the images/figures. • Traceability: Version of the document, prompts, methodological choices and substantial changes; allow for reproducibility. • Privacy and Security: Protect sensitive information; maintain strong de-identification. • Training: Teach authors, reviewers, and editors about what they can and can’t do with AI. • Critical Assessment: All AI outputs should be tested against methodological and clinical benchmarks; AI is a helper, not a judge. • Red Lines: Plagiarism, made-up references, or unverifiable hallucinations; apply appropriate sanctions. Our goal as surgeons and medical scientists is to promote quality care and improve patient outcomes based on the best available evidence. If these principles, of transparency, traceability, integrity of data, verification and privacy, are respected, then the primary question is not whether AI “participated”, but whether the knowledge that came after is valid, useful and applicable to improve practices. The authors have the intellectual authorship and the clinical judgment; AI is the instrument we use to improve and fine-tune. Priorities should centre on aligning decisions with high-quality evidence, with critical appraisal of bias and benefit–harm, rather than ritual scrutiny of the tool used to reach the result. Some academic societies are already making progressing in this direction. The European Society for Gynaecological Endoscopy, which is one of the surgical societies at the forefront of minimally invasive gynaecologic surgery, created a SIG on AI. The American Association of Gynecologic Laparoscopists formed an AI Task Force. The goals of these academic societies include education, project development, as well as ethical and medico- legal discussion about institutional and professional use. This, I think, is the right route to take: not rejection, but acceptance with discernment, adjustment, and improvement. What about the near future? Early prototypes of more autonomous surgical robots are emerging.4 They remain imperfectly implemented and must still operate under strict human supervision, but they are there. In the beginning, the majority of patients are likely to trust and give preference to their surgeon, but subsequent generations, who have grown up with this technology, will see nothing unusual in it. Adoption is inevitable, and responsibility lies in arriving prepared using standards, audits, and a culture of safety. AI is not a shortcut to think less, just as calculators were not a shortcut to understand less mathematics. It is a tool that allows us to spend more human intellect to what matters, like spending more time with our patients or improving our surgical skills. If our shared goal is to improve practice and deliver the best evidence-based care, the question is not whether we allow AI, but how we incorporate it so that it raises quality, saves time, and expands equity, whilst yielding nothing on ethics, rigour, and accountability. Let’s adapt before we fall behind.

None. Contributors: Writing: S.H. Funding: None. Competing interests: None. Ethical approval: Not needed. Informed consent: Not needed. Data sharing: No shared data. Transparency: I affirm that the manuscript is honest, accurate, and transparent.

1. National Council of Teachers of Mathematics. Position statements. Math Teach. 1978;71:468. 2. Flanagin A, Kendall-Taylor J, Bibbins-Domingo K. Guidance for Authors, Peer Reviewers, and Editors on Use of AI, Language Models, and Chatbots. JAMA. 2023;330:702-3. 3. van Dijk SHB, Brusse-Keizer MGJ, Bucsán CC, van der Palen J, Doggen CJM, Lenferink A. Artificial intelligence in systematic reviews: promising when appropriately used. BMJ Open. 2023;13:e072254. 4. Saeidi H, Opfermann JD, Kam M, Wei S, Leonard S, Hsieh MH, et al. Autonomous robotic laparoscopic surgery for intestinal anastomosis. Sci Robot. 2022;7:eabj2908.