Navigating the complexities of artificial intelligence in scientific writing: a dual perspective

• Published in: International journal of gynecological cancer Vol. 34; no. 10; pp. 1495 - 1498
• Main Authors: Levin, Gabriel, Piedimonte, Sabrina, Zand, Behrouz
• Format: Journal Article
• Language: English
• Published: United States BMJ Publishing Group Ltd 01.10.2024; Elsevier Inc; Elsevier Limited
• Subjects: Accountability; Algorithms; Artificial Intelligence; Bias; Carcinoma, Ovarian Epithelial; Cervical Cancer; Clinical trials; Decision making; Editorial; Efficiency; False information; Genital cancers; Humans; Large language models; Medical practices; Medical screening; Oncology; Peer review; Plagiarism; Privacy; Scholarly publishing; Transparency; Trends; Uterine Cancer; Vagina; Vulvar and Vaginal Cancer; Writing; Writing - standards; Carcinoma, Ovarian Epithelial; Uterine Cancer; Vulvar and Vaginal Cancer; Cervical Cancer
• ISSN: 1048-891X; 1525-1438; 1525-1438
• DOI: 10.1136/ijgc-2024-005691

Correspondence to Dr Behrouz Zand, Gynecologic Oncology, Houston Methodist, The Woodlands, Texas, USA; bzand@houstonmethodist.org Artificial intelligence (AI) is rapidly being integrated into many aspects of patient care in oncology,1 and presents unique opportunities to improve predictions in all aspects including cancer screening, diagnosis, treatment, surgical outcomes, recurrence, and survival.2 Beyond the advancements in utilizing AI in clinical oncology, AI is also widely used by scholars and researchers.3 Recently, the publication of scientific medical literature has seen a significant rise, with an annual growth rate of approximately 10%.4 However, this rise driven by the competitive pressure of ‘publish or perish’ in academia may lead to the potential of authorship misconduct and misrepresentation.5 The recent introduction of generative artificial intelligence (GAI) carries the potential both to revolutionize scientific writing and to be ethically misused.6 Current editorial and peer-review systems are already strained by the overwhelming influx of publications and may not be able to stand guard against the potential misuse of GAI in scientific writing. By automating these processes, AI frees editors to focus on nuanced decision-making. [...]AI optimizes reviewer selection and enhances search result sorting in databases like PubMed, enhancing research accessibility.9 AI-powered summarizers and translators make complex articles more accessible, benefiting scholars of all levels, including those hindered by language barriers.10 11 In oncology, AI expedites literature reviews, meta-analyses, and systematic reviews bolstering research efficiency and accuracy.12 Tools such as Scholarcy and Scite offer concise summaries, highlight key studies, and identify areas for further investigation.13 14 Text mining tools like Scopus and Web of Science aid in trend analysis and identifying emerging research themes.15 AI also facilitates predicting future trends and efficiently matches manuscripts with suitable journals, optimizing the submission process for increased publication success.16 Additionally, AI’s capacity for analyzing big data accelerates discovery by uncovering hidden trends and patterns.17–19 However, this efficiency comes with trade-offs. The misuse of GAI tools to fabricate or manipulate data could undermine the credibility of academic work.26 Their ability to produce text indistinguishable from human-written content simplifies the manuscript writing process, rendering it effortless – further straining the overloaded publishing system.7 27 Privacy issues, particularly data used to train AI models, are also of significant concern.28 29 For instance, the Royal Free NHS Trust granted Google’s DeepMind access to over a million NHS patient records without patient consent, sparking significant privacy debates and legal challenges.30 The opaque nature of AI algorithms, often referred to as a ‘black box,’ exacerbates privacy challenges, especially in handling personal medical information. GPT-4’s tendencies to exaggerate disease prevalence differences among demographic groups, perpetuate stereotypes, and amplify harmful biases are particularly troubling. [...]there is a natural tendency for AI benefits to favor more privileged populations over those in rural or underserved areas.