Peer review has been the gatekeeper of research for decades. However, this crucial process faces a numerous issues including, but not limited to, bias, lack of transparency and accountability, oversight of ethical concerns in a manuscript, and extreme delays. Moreover, reviewers are often overburdened with their academic and research responsibilities in addition to their review duties. Therefore, there is a growing need to adopt innovative peer review models that seamlessly enhance the efficiency of the process and keep pace with the growing volume of research output.<\/p>\n

Innovative Models in Peer-Review<\/h2>\n

Open Peer Review Model<\/h3>\n

Contrary to the traditional single-blind and double-blind peer review models that advocate anonymity, open peer-review model promotes Open Science by making the reviewer\u2019s identity publicly available. Various journals in life sciences, including Nature Communications, EMBO, Royal Society Open Science, eLife,<\/em> and the PLOS journals<\/em> have implemented this model.
\nIn the open peer review model, the review reports are published alongside the article, enabling documentation of reviewer comments and the authors\u2019 responses. As a result, this approach improves transparency and review quality, promotes accountability, and minimizes potential biases.<\/p>\n

Pre-publication Peer Review Model<\/h3>\n

Pre-publication peer review model leverages the expertise of the research community to review and share constructive criticism on a manuscript even before it is submitted to journals. This model allows authors to address the gaps or oversights identified by contemporaries in their field before submitting their manuscript to a journal for publication. Therefore, this helps in avoiding multiple rounds of revisions and resubmissions. Furthermore, it facilitates rapid dissemination of research, increases visibility, fosters community feedback, and contributes to the principles of open science.
\nPreprints is as another example of pre-publication peer review model. The first preprint server, arXiv.org<\/a>, launched in 1991, gained significant traction particularly during the pandemic, with the introduction of medRxiv<\/a> in 2019. This highlights the timely need for quicker and transparent methods of sharing crucial research findings within the community.<\/p>\n

Post-publication Peer Review Model<\/h3>\n

Scrutiny of research articles doesn\u2019t stop once they\u2019re published; in fact, it intensifies in the months that follow! While post-publication peer review (PPPR) has existed in multiple journals in the form letters and commentaries, a modern version of this model enables quicker and more dynamic discussions within the scientific community.
\nPlatforms like Retraction Watch<\/a>, ResearchGate<\/a>, PubPeer<\/a>, and social media (including X and LinkedIn) have become popular in promoting post-publication peer review, allowing researchers to engage in real-time dialogue. This model serves as a quality checkpoint to identify ethical issues such as data manipulation, statistical errors, and fundamental flaws that might otherwise go unnoticed. Detecting these issues is crucial as this allows the publication community to prevent the dissemination of false claims by retracting such articles. Over 10,000 papers got retracted last year and PPPR plays a central role in this by upholding the integrity of the publication industry.<\/p>\n

AI-assisted Peer Review<\/h3>\n

The automated peer review model, also known as the AI-assisted peer review model, aims at reducing the reviewers\u2019 burden and fatigue by screening the manuscript for initial checks and detecting common flaws in manuscripts. This enables reviewers to reduce their time on the preliminary checks and focus on the scientific underpinnings of the study for a more thorough and critical evaluation.
\nA preprint<\/a> reported that the use of AI in the peer-review process can improve the review quality and streamline the workload for reviewers, ultimately boosting the speed and review efficiency.
\nTools like Enago Read<\/a>, has been developed and tested for their performance over the last few years. With proper human oversight, these tools promise to lead the next generation of the peer-review process.<\/p>\n

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