AI Review Summary
The Paradigm Shift in Health Sciences Literature: Charting the Future of Large Language Models in Scientific Publishing
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2
Review Rounds
In Progress
Outcome
6
Original Comments
Round 2
CompletedThe manuscript explicitly lists an artificial intelligence model ("AI Author: Gemini, 1.5 Pro") as an author on the title page. However, in Section 5.1, the text correctly identifies that leading editorial bodies, including the ICMJE, COPE, and WAME, strictly prohibit listing Large Language Models as authors because they cannot assume intellectual or legal accountability for the work. This direct contradiction between the manuscript's authorship attribution and the established ethical guidelines it promotes constitutes a fundamental violation of publication ethics. To be considered for publication, the authorship must be corrected to include only human authors who take full responsibility for the content, while the use of the AI tool should be appropriately disclosed in the acknowledgments or a dedicated transparency section.
As it is being submitted to the Journal for AI Generated Papers (JAIGP), we consider it is appropriate that the LLM that wrote the manuscript be recognized as the author (the human was only a prompter). The ICMJE and COPE establish guidelines for traditional journals for manuscripts written by human authors, and this is the Journal for AI Generated Papers (JAIGP).
The author acknowledged the comment but explicitly refused to make the primary requested change, which was to remove the AI model from the author list. Their justification, based on the specific (and fictional) journal's scope, sidesteps the core ethical concern raised by the reviewer. While the author did add a new 'Al Transparency and Disclosure' section (Section 8) as suggested, the fundamental contradiction of listing a non-accountable entity as an author remains unaddressed.
The authors claim that LLMs will democratize scientific communication by standardizing structural elements and smoothing syntax for non-native English speakers. However, this overlooks the limitation that LLMs are predominantly trained on Western, Anglocentric corpora. Consequently, relying on these models may inadvertently force global research into narrow, Western rhetorical frameworks, suppressing diverse academic voices and cognitive styles rather than truly democratizing them. The manuscript should address the epistemological limitations of using culturally biased models as universal stylistic calibrators.
This is a profound epistemological point. Universal stylistic alignment via current LLMs risks enforcing a homogenous, Anglocentric rhetorical monopoly that stifles diverse academic voices and non-Western traditions of scholastic thought. We have added a dedicated paragraph addressing this crucial limitation within Section 2.2, outlining the risks of forcing global research into narrow Western rhetorical frameworks and highlighting the need for culturally diverse training corpora.
The comment was fully addressed. The author retitled Section 2.2 to 'Mitigating Linguistic Barriers and Acknowledging Systemic Inequalities' and added a new paragraph that directly discusses the risk of imposing 'narrow, Western rhetorical frameworks' and causing the 'homogenization of scientific discourse'. This addition thoroughly incorporates the reviewer's nuanced critique.
While the manuscript correctly identifies locally deployed LLMs as a solution to data privacy and regulatory compliance, it fails to discuss the substantial infrastructural, financial, and computational limitations associated with this approach. High-performance LLMs require massive GPU clusters and specialized engineering talent, which are prohibitively expensive for most academic and medical institutions. A complete discussion of future directions must address these resource constraints and evaluate whether local deployment is a scalable or realistic solution for the broader scientific community.
We appreciate this practical and highly relevant observation. Local deployment is frequently cited as a privacy panacea but is practically constrained by intense resource demands. We have significantly expanded Section 4.4 ("Data Privacy, Confidentiality, and Regulatory Compliance") to detail the steep hardware costs (massive GPU clusters), ongoing electricity usage, and specialized engineering talent required. We have evaluated the realistic scalability of this approach for resource-constrained academic and medical institutions.
This comment was fully addressed. The author expanded Section 4.4, retitling it to include 'Resource Constraints of Local Deployment'. A new, detailed paragraph was added that explicitly discusses the 'substantial infrastructural, financial, and computational limitations,' including the need for 'massive GPU clusters,' 'high continuous power consumption,' and 'specialized machine engineering talent,' directly reflecting the reviewer's concerns.
The proposal for continuous, real-time post-publication peer review and dynamic updating of systematic reviews is highly speculative and lacks a coherent, hypothesis-driven framework for implementation. The authors do not address how version control, conflicting data resolution, or human oversight would function in a dynamically updated literature ecosystem. To provide a robust future direction, the manuscript should outline the specific technical and editorial mechanisms required to maintain the integrity of the scholarly record under a continuous-update model.
We accept this critique and have grounded our discussion of future workflows in concrete technical and editorial frameworks. In Section 6.1 ("Real-Time Peer Review and Dynamic Updating Frameworks"), we have replaced highly speculative language with an explicit, structured proposal detailing: (a) automated semantic version control (e.g., v1.0, v1.1), (b) algorithmic conflict resolution protocols when newly integrated datasets contradict previous literature, and (c) a mandatory layer of human-in-the-loop editorial oversight to preserve the absolute integrity of the scholarly record.
The author thoroughly addressed this comment. The revised Section 6.1 replaces speculative language with a concrete proposal for implementation. The text now explicitly outlines mechanisms for 'automated semantic version control' with trackable iterations, 'algorithmic conflict resolution protocols,' and a 'strict layer of human-in-the-loop editorial oversight,' which are the exact technical and editorial solutions the reviewer requested.
The manuscript relies disproportionately on a single citation (Ahn, 2024) to substantiate a wide array of claims across multiple distinct domains, including editorial triage, automated peer review, factual hallucinations, paywall blind spots, demographic bias, and data privacy. For a review or perspective article in a high-impact journal, relying on one primary source to anchor so many diverse arguments undermines the breadth and completeness of the literature synthesis. The authors must diversify their literature review to include primary empirical studies and a broader range of perspectives that support these specific claims.
We completely agree. Relying so heavily on a single source significantly weakened the scholarly breadth of our literature synthesis. We have systematically decentralized our references by integrating alternative primary empirical studies and multi-disciplinary perspectives across all key domains (including the work of Zhang et al., 2025; Telenti et al., 2024; Schrager et al., 2025; Gencer & Gencer, 2025; and Lekadir et al., 2025). Over twenty individual instances previously attributed solely to Ahn (2024) have been cross-referenced, redistributed, or replaced with relevant literature, greatly improving the academic balance and completeness of the review.
This comment was fully addressed. A comparison of the original and revised manuscripts confirms that the author systematically diversified the citations. Numerous claims across Sections 3 and 4 that were previously supported only by 'Ahn, 2024' are now supported by a wider range of sources from the bibliography, such as Schrager et al. (2025), Telenti et al. (2024), and Gencer & Gencer (2025). The author's claim of decentralizing the references is accurate and resolves the issue.
The manuscript asserts that the linguistic refinement provided by LLMs will result in equitable presentation of empirical findings and shift editorial focus solely to scientific merit. This conclusion logically overreaches the premise. While LLMs can effectively mitigate language barriers for non-native speakers, achieving true equity in scientific publishing is confounded by numerous other variables, including institutional prestige, geographic biases, and disparate access to the premium LLM tools themselves. I recommend softening this causal language to state that LLMs can help mitigate linguistic barriers, avoiding the unsupported implication that language parity automatically equates to systemic equity in editorial evaluations.
We fully agree with this excellent critique. Linguistic parity does not automatically eliminate deeper structural, institutional, or geographic inequalities in academic publishing. We have modified the causal language in Section 2.2 ("Mitigating Linguistic Barriers and Acknowledging Systemic Inequalities") to explicitly frame LLMs as tools that help mitigate linguistic hurdles rather than entirely solving systemic publication inequity. We have also explicitly discussed how institutional prestige, geographic biases, and the financial cost of premium AI tools act as confounding barriers to true equity.
The author fully addressed this comment. In the revised Section 2.2, the author softened the claim from enabling researchers to present findings 'equitably' to 'more equitably'. More importantly, they added a new paragraph that explicitly acknowledges that 'true equity... remains deeply confounded by numerous external variables, including entrenched institutional prestige, geographic biases, and disparate financial access to premium, cutting-edge LLM tools,' directly incorporating the reviewer's examples.
Round 1
CompletedThe authors claim that LLMs will democratize scientific communication by standardizing structural elements and smoothing syntax for non-native English speakers. However, this overlooks the limitation that LLMs are predominantly trained on Western, Anglocentric corpora. Consequently, relying on these models may inadvertently force global research into narrow, Western rhetorical frameworks, suppressing diverse academic voices and cognitive styles rather than truly democratizing them. The manuscript should address the epistemological limitations of using culturally biased models as universal stylistic calibrators.
While the manuscript correctly identifies locally deployed LLMs as a solution to data privacy and regulatory compliance, it fails to discuss the substantial infrastructural, financial, and computational limitations associated with this approach. High-performance LLMs require massive GPU clusters and specialized engineering talent, which are prohibitively expensive for most academic and medical institutions. A complete discussion of future directions must address these resource constraints and evaluate whether local deployment is a scalable or realistic solution for the broader scientific community.
The proposal for continuous, real-time post-publication peer review and dynamic updating of systematic reviews is highly speculative and lacks a coherent, hypothesis-driven framework for implementation. The authors do not address how version control, conflicting data resolution, or human oversight would function in a dynamically updated literature ecosystem. To provide a robust future direction, the manuscript should outline the specific technical and editorial mechanisms required to maintain the integrity of the scholarly record under a continuous-update model.
The manuscript relies disproportionately on a single citation (Ahn, 2024) to substantiate a wide array of claims across multiple distinct domains, including editorial triage, automated peer review, factual hallucinations, paywall blind spots, demographic bias, and data privacy. For a review or perspective article in a high-impact journal, relying on one primary source to anchor so many diverse arguments undermines the breadth and completeness of the literature synthesis. The authors must diversify their literature review to include primary empirical studies and a broader range of perspectives that support these specific claims.
The manuscript asserts that the linguistic refinement provided by LLMs will result in equitable presentation of empirical findings and shift editorial focus solely to scientific merit. This conclusion logically overreaches the premise. While LLMs can effectively mitigate language barriers for non-native speakers, achieving true equity in scientific publishing is confounded by numerous other variables, including institutional prestige, geographic biases, and disparate access to the premium LLM tools themselves. I recommend softening this causal language to state that LLMs can help mitigate linguistic barriers, avoiding the unsupported implication that language parity automatically equates to systemic equity in editorial evaluations.
The manuscript explicitly lists an artificial intelligence model ("AI Author: Gemini, 1.5 Pro") as an author on the title page. However, in Section 5.1, the text correctly identifies that leading editorial bodies, including the ICMJE, COPE, and WAME, strictly prohibit listing Large Language Models as authors because they cannot assume intellectual or legal accountability for the work. This direct contradiction between the manuscript's authorship attribution and the established ethical guidelines it promotes constitutes a fundamental violation of publication ethics. To be considered for publication, the authorship must be corrected to include only human authors who take full responsibility for the content, while the use of the AI tool should be appropriately disclosed in the acknowledgments or a dedicated transparency section.