AI evidence extraction

Human-AI Collaboration in Peer Review Manuscript

Advances in medical education, research, and ethics (AMERE) book series · 2024

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.4018/979-8-3693-3828-5.ch003

Methodology & findings

Study design

Literature review examining the collaboration between human reviewers and AI in the academic peer review process

Main result

The review points out how "AI has the potential to enhance the efficiency of manuscript assessment and address challenges such as increasing submission volumes and a shortage of reviewers." Current AI tools support "tasks such as plagiarism detection, formatting checks, and initial quality control, while more complex tasks requiring human empathy and robust judgment remain challenging for AI."

Reports effect sizes.

Research paradigm

interpretivist

Author conclusions

The authors conclude that while "AI can potentially improve the speed and precision of the review process," it "also presents ethical and legal concerns, including biases, data privacy, and copyright issues." They emphasize that "These concerns require thorough examination and the development of comprehensive ethical guidelines."

Limitations

The abstract indicates that "more complex tasks requiring human empathy and robust judgment remain challenging for AI" and that concerns about "biases, data privacy, and copyright issues" require "thorough examination and the development of comprehensive ethical guidelines," suggesting limitations in current AI capabilities and regulatory frameworks.

Open questions raised

The need for comprehensive ethical guidelines addressing biases, data privacy, and copyright issues in AI-assisted peer review; the development of AI systems capable of handling complex tasks requiring human empathy and robust judgment.
The paper identifies the need for comprehensive ethical guidelines addressing AI biases, data privacy concerns, and copyright issues in peer review processes.
The development of comprehensive ethical guidelines for AI in peer review, and further exploration of how to balance AI efficiency gains with human judgment in complex manuscript assessment tasks.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 87%

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