Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review

• Published in: Clinics and practice Vol. 14; no. 1; pp. 89 - 105
• Main Authors: Miao, Jing, Thongprayoon, Charat, Suppadungsuk, Supawadee, Garcia Valencia, Oscar A., Qureshi, Fawad, Cheungpasitporn, Wisit
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.12.2023; MDPI
• Subjects: academic integrity and AI; AI-enhanced plagiarism detection; Artificial intelligence; artificial intelligence in academia; Automation; Chatbots; Chronic illnesses; Cognition & reasoning; Coronaviruses; COVID-19; Credibility; Electrolytes; ethical dilemmas in scholarly writing; Ethics; Language; Medical research; Natural language processing; Nephrology; nephrology research ethics; Peer review; Problem solving; Research methodology; Review; Scholarly publishing; Writing; academic integrity and AI; nephrology research ethics; ethical dilemmas in scholarly writing; AI-enhanced plagiarism detection; artificial intelligence in academia
• ISSN: 2039-7283; 2039-7275; 2039-7283
• DOI: 10.3390/clinpract14010008

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI’s capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an “AI scrutiny” phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI’s role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI’s involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI’s capabilities while upholding stringent academic standards can be envisioned.