ReviewTaiwan journal of ophthalmology
Generative artificial intelligence in ophthalmology research writing: A comprehensive review of applications, detection tools, and ethical considerations.
Review in Taiwan journal of ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rise of generative artificial intelligence (GenAI) has profoundly influenced medical research and academic writing, particularly in ophthalmology. Despite its growing relevance, there is a noticeable gap in the literature regarding its application in medical writing, including practical uses and associated limitations. This review seeks to fill in this gap by first systematically reviewing the current literature on GenAI in medical paper writing. It identifies and discusses nine key applications and considerations, including idea generation, literature review, institutional review board preparation, data collection, data analysis, image generation, manuscript drafting, writing refinement, and peer review. In the second part, we explore publicly available AI tools that currently assist with medical manuscript writing. We also introduce several generative AI detection tools and discuss their accuracy and reliability. Finally, the review addresses the limitations and ethical challenges associated with the use of GenAI in medical paper writing. While GenAI has streamlined many aspects of medical paper writing, and an increasing number of AI tools have been developed for research, significant model limitations and ethical concerns persist, necessitating careful human oversight and clear guidelines. By providing a comprehensive yet focused overview, this article offers valuable insights into the effective use of GenAI in medical paper writing while acknowledging its limitations and risks. It aims to support researchers in producing high-quality, AI-enhanced publications in the field of ophthalmology.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.