AI evidence extraction

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

Jing Miao, Charat Thongprayoon, Supawadee Suppadungsuk, Oscar A. Garcia Valencia, Fawad Qureshi, Wisit Cheungpasitporn · Clinics and Practice · 2023

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

3.29

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.3390/clinpract14010008

Methodology & findings

Study design

Narrative review using qualitative synthesis of existing literature, case examples, and expert consensus on ethical dilemmas in AI-assisted academic writing in nephrology academia.

Main result

The paper identifies that "Scholars have been caught red-handed, incorporating verbatim text from AI language models into their peer-reviewed articles" and finds that "this malpractice has been detected across a spectrum of journals, from lesser-known outlets to those with substantial academic influence." Additionally, the review reveals that "a prospective cross-sectional global survey in urology showed that among 456 urologists, almost half (48%) of them use ChatGPT or other large language models for medical research, with fewer (20%) using the technology in patient care, and more than half (62%) thinking there are potential ethical concerns when using ChatGPT for scientific or academic writing."

Research paradigm

Interpretivist/Critical

Author conclusions

The authors conclude that "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." They further state: "Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned."

Risk of bias

Selection bias in case examples cited (may represent most egregious cases rather than representative sample); Potential confirmation bias in reporting instances of AI misuse; Limited nephrology-specific data, requiring extrapolation from other fields; Absence of systematic search methodology for identifying relevant literature; Potential selection bias in literature review (non-systematic search strategy); Publication bias (focus on detected cases of AI misuse, may not represent actual prevalence); Author perspective bias (nephrology-focused institution, may overweight nephrology examples); Confirmation bias in case selection (examples chosen may emphasize problems); Selection bias: No systematic search protocol specified; studies selected at author discretion; No quality assessment of included studies reported; Confirmation bias: Review focuses primarily on negative cases and ethical concerns; Disciplinary bias: Heavy focus on nephrology-specific examples may not generalize; Publication bias: Likely skewed toward published studies highlighting AI problems

Limitations

The paper acknowledges that "While there is no research indicating the extent of AI tool usage in nephrology-related academic theses, the increasing application of these tools in this field is noteworthy." Additionally, the authors note that "At present, the degree to which AI tools are employed in nephrology grant applications is unclear, yet given the rapid rise in AI adoption, attention should be drawn to this area." The review also states that "further research and guidance are essential in this domain" regarding the use of AI as a tool for journal editors and reviewers.

Open questions raised

Need for rigorous research to assess the extent of AI's involvement in the academic literature
Need to evaluate the effectiveness of AI-enhanced plagiarism detection tools
Need to understand the long-term consequences of AI utilization on academic integrity
Lack of clarity on the extent of AI tool usage in nephrology-related academic theses and grant applications
Insufficient empirical evidence on the effectiveness of current AI detection tools
Limited understanding of systemic vulnerabilities in the academic publishing domain

Extracted from: pdfAgreement 68%

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