Targeted literature searches are a fundamental part of writing clinical manuscripts that will meet the standards of high-quality journals and contribute meaningfully to evidence-based practice. When physicians write clinical manuscripts, utilizing a targeted literature search can identify high-quality, relevant, and current evidence. While a general literature review is useful, a targeted literature search is specific to the clinical question and should be completed through frameworks established, such as PICO (Population, Intervention, Comparator, Outcome) and PRISMA [1].

AI and Peer Review: Collaborative Intelligence and Human Expertise

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AI and Peer Review: Collaborative Intelligence and Human Expertise

Table of Content
1. Potential Applications of AI in Peer Review
2. Potential Problems of AI in Peer Review
3. Current Views and Guidelines on AI Use in Peer Review
4. Looking Ahead: Important Considerations
5. Conclusion

Peer review is a fundamental component of ensuring the quality and integrity of research. There are, however, issues with peer review; along with the concept of reviewer fatigue, other issues can include long turnaround times and subjective bias. Artificial intelligence (AI) offers avenues to augment human capacity and expertise, increase efficiency of processes, and improve transparency in peer review. This article will examine how AI may assist, disrupt, and change peer review. [1]

1. Potential Applications of AI in Peer Review

AI can play a crucial role in several stages of peer review, assisting editors, reviewers, and authors alike. [2]

Stage	AI Function	Benefits
Pre-screening	Plagiarism checks, formatting verification, and ethical compliance	Saves editorial time and reduces desk rejections
Reviewer Matching	Automated reviewer selection using topic modeling and bibliometric data	Improves reviewer manuscript fit
Content Summarization	Generating abstracts or summaries of long manuscripts	Helps editors and reviewers quickly grasp key ideas
Quality Assessment	Detecting data inconsistencies, citation anomalies, and figure manipulation	Enhances integrity and reproducibility
Sentiment & Bias Detection	Identifying biased or unconstructive reviewer comments	Promotes fairness and professionalism

2. Potential Problems of AI in Peer Review

Although AI brings changes in efficiency, it brings questions regarding ethics and process as well. [3]

Challenge and Risks

Data Privacy: The content of a manuscript may still include data that has yet to be published, or confidential data that AI systems could act on.
Bias: AI models based on biased training data may reinforce systemic inequities in the selection of reviewers or in their evaluations.
Lack of Transparency: Many AI systems are black boxes, making any decision made difficult to interpret or audit.
Over-Reliance: Editors may have an excessive level of trust in AI outputs with less than adequate human review.
Plagiarism or Data Leaky: Using generative AI tools may result in author text being leaked or re-generated text that can impair an organization or the review process.

3. Current Views and Guidelines on AI Use in Peer Review

Publishing organizations are beginning to establish policies for responsible AI use:[4]

Organization	Guideline Summary
COPE (Committee on Publication Ethics)	AI should not be listed as a reviewer; reviewers must disclose any AI assistance.
Nature Publishing Group	AI tools may be used only for grammar or summary assistance, not for evaluation.
Elsevier	Reviewers using AI must ensure confidentiality and accuracy and disclose its use.

3.1. Common Guiding Principles

Human responsibility must continue to be the focus.
AI utilization should be transparent and always be limited to supportive modalities and never substitute expert judgment.

4. Looking Ahead: Important Considerations

Building Explainable AI (XAI): Models must provide valid and credible reasons for their recommendations [5]

Ethical oversight: Journals may need boards or checklists on the ethics of AI
Training reviewers: Researchers should learn to use AI in a responsible way
Global equity: AI should not disadvantage researchers in underrepresented geographies or languages.

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Conclusion

AI has the potential to transform the peer review process through collaborative intelligence, enhancing the process’s efficiency while still being overseen by humans. The future of peer review will involve an appropriate relationship of enhancing decisions through AI as a powerful tool, informed by human ethics, transparency, and expertise.

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References

Overcash J. (2025). Human Expertise in an AI-Collaborative Peer-Review Process. Oncology nursing forum, 52(5), 316–317. https://doi.org/10.1188/25.ONF.316-317
Doskaliuk, B., Zimba, O., Yessirkepov, M., Klishch, I., & Yatsyshyn, R. (2025). Artificial Intelligence in Peer Review: Enhancing Efficiency While Preserving Integrity. Journal of Korean medical science, 40(7), e92. https://doi.org/10.3346/jkms.2025.40.e92
Kemal Ö. (2025). Artificial Intelligence in Peer Review: Ethical Risks and Practical Limits. Turkish archives of otorhinolaryngology, 63(3), 108–109. https://doi.org/10.4274/tao.2025.2025-8-12
Leung, T. I., de Azevedo Cardoso, T., Mavragani, A., & Eysenbach, G. (2023). Best practices for using AI tools as an author, peer reviewer, or editor. Journal of Medical Internet Research, 25, e51584. https://doi.org/10.2196/51584
What is explainable AI (XAI)? (2025, July 29). com. https://www.ibm.com/think/topics/explainable-ai