Large language models (LLMs) now assist with drafting, translation, literature searches, and peer-review preparation. Such use is not inherently problematic: Supervised assistance can improve clarity and reduce language barriers. The concern is that undisclosed assistance obscures how a scientific text was produced and who is accountable for it.
Quick Take
Large language models (LLMs) are increasingly being integrated into ophthalmic publishing for tasks ranging from manuscript drafting and translation to peer-review preparation. While these tools can enhance clarity and bridge language gaps, the primary concern remains the lack of transparency regarding their use and the resulting ambiguity in authorship accountability.
Why It Matters
As AI becomes a standard tool in medical writing, the integrity of ophthalmic literature depends on clear boundaries between human expertise and machine assistance. For clinicians who rely on published research to guide evidence-based patient care, knowing that a human expert is fully accountable for the data and conclusions is paramount. Undisclosed AI use risks introducing subtle hallucinations or biases that could undermine the scientific record if not properly supervised and reported.
Key Details
Practice Implications
Researchers and clinicians contributing to ophthalmic journals should adopt a policy of radical transparency, documenting exactly how and where LLMs were used in the preparation of any work. When consuming new research, professionals should look for disclosure statements to better assess the level of human oversight involved in the study's presentation and analysis.
Who's Affected
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