Large language models to write scientific manuscripts: to be considered but not trusted

Large language models (LLMs) are rapidly permeating scientific writing. Using ChatGPT 4o and real-world data from the Swedish Heart Failure Registry, we explored both the promise and the pitfalls of LLM-assisted manuscript drafting. We demonstrate that, with strict human oversight, LLMs may accelerate text generation but still hallucinate, frequently misinterpret effect directions, and are often unable to access other manuscripts for direct references. Until these limitations are addressed, LLMs should complement, rather than replace, domain experts in scientific publishing.

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Publication Details

Journal
Global Cardiology
Published
2025-06-30
DOI
https://doi.org/10.4081/cardio.2025.74
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
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article

Large language models to write scientific manuscripts: to be considered but not trusted

Gianluigi Savarese, Christian Basile, Stefan D. Anker
Global Cardiology
Topic Modeling
article

Large language models to write scientific manuscripts: to be considered but not trusted

Gianluigi Savarese, Christian Basile, Stefan D. Anker
article en

Abstract

Large language models (LLMs) are rapidly permeating scientific writing. Using ChatGPT 4o and real-world data from the Swedish Heart Failure Registry, we explored both the promise and the pitfalls of LLM-assisted manuscript drafting. We demonstrate that, with strict human oversight, LLMs may accelerate text generation but still hallucinate, frequently misinterpret effect directions, and are often unable to access other manuscripts for direct references. Until these limitations are addressed, LLMs should complement, rather than replace, domain experts in scientific publishing.

Global CardiologyVol. 3(2)
Stockholm South General Hospital (SE), German Centre for Cardiovascular Research (DE)
Openalex Percentile: Top 91%
Topic Modeling
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