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Clarity Without Credibility? Human Versus AI Abstracts in Otolaryngology
Sholem Hack1, Rebecca Attal1, Lirit Levi2
1City St. George's University London School of Medicine, Program Delivered by University of Nicosia at the Chaim Sheba Medical Center Ramat Gan Israel.
Summary
Otolaryngologists struggled to differentiate between human and AI-generated abstracts, with machine learning models showing improved writing but lower perceived usefulness. Clinicians demonstrated moderate accuracy in identifying authorship, highlighting AI
Area of Science:
- Medical Writing
- Artificial Intelligence in Healthcare
- Scientific Communication
Background:
- Large language models (LLMs) are increasingly used for scientific writing.
- Assessing the quality and detectability of AI-generated abstracts is crucial.
Purpose of the Study:
- To evaluate otolaryngologists' ability to distinguish human-written from AI-generated abstracts.
- To compare the clarity and usefulness of LLM-generated abstracts against human-authored ones.
- To determine the accuracy of authorship identification by medical reviewers.
Main Methods:
- A blinded, cross-sectional study evaluated 48 abstracts (24 human, 24 AI-generated).
- Twenty otolaryngologists rated abstracts on clarity, usefulness, and authorship using a structured rubric.
- Statistical analyses included chi-square, Mann-Whitney, and Kruskal-Wallis tests.
Main Results:
- Overall authorship recognition accuracy was 44.7%, with human abstracts more often misclassified as AI.
- Human abstracts scored higher in clarity and usefulness than LLM abstracts (small effect sizes).
- AI models like GPT-5 and Claude 3.5 produced abstracts more similar to human writing than Grok.
Conclusions:
- LLMs generate abstracts that mimic human scientific writing but lack perceived usefulness and credibility.
- Clinicians showed only moderate success in detecting AI authorship, often with misplaced confidence.
- Findings underscore the evolving capabilities and potential risks of AI in scientific communication.
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