The realistic future is not AI replacing peer reviewers, but AI becoming an embedded infrastructure that handles the heavy lifting, while human reviewers focus on interpretation, originality, and...

AI, the Peer Review Crisis, and the Path to a Responsible Future

by ChatGPT-5

Introduction

Peer review — the cornerstone of scholarly publishing — is under unprecedented strain. Rising submission volumes, reviewer fatigue, and concerns over review quality have converged into a systemic crisis. At the same time, AI tools are emerging that could radically transform the process, from editing and fact-checking to fully automated reviews. This dual reality creates both hope and unease. The challenge is to reconcile the efficiency gains of AI with the ethical, intellectual, and social integrity of peer review.

Current Challenges

1. Overload and Reviewer Fatigue

• Submission volumes have risen sharply, especially post-COVID, while the reviewer pool remains stagnant.

• Editors are forced to send more invitations to secure the same number of reviews, and turnaround times are increasing.

• The bulk of reviewing is done by a small fraction of scientists — in some studies, 20% do up to 94% of all reviews.

2. Quality and Reliability Issues

• Reports can be superficial, poorly written, or biased.

• Critical errors — methodological, statistical, or factual — often slip through.

• Some journals publish research of questionable rigor, eroding trust.

3. Inefficient Systems

• Duplicate reviewing of rejected manuscripts wastes effort.

• Informal institutional pre-reviews add hidden burdens.

• Grant review processes can be slow and exclusionary, sometimes reinforcing gatekeeping by senior academics.

4. Incentive Gaps

• Reviewing is often unpaid and undervalued in career assessments.

• Attempts at financial rewards have had mixed results due to budget constraints and concerns about bias.

5. AI-related Risks

• Undisclosed use of generative AI for reviews, sometimes in violation of publisher policies.

• Potential confidentiality breaches when manuscripts are uploaded to public AI platforms.

• Risk of shallow, homogenized “template” reviews and an AI-driven echo chamber where AI-written papers are reviewed by AI.

• Difficulty detecting AI-generated reviews, undermining transparency and accountability.

Addressing the Challenges

1. Expanding and Diversifying the Reviewer Pool

• Train early-career researchers through joint reviews with senior scientists.

• Use AI-matching tools (integrated with databases like Scopus) to identify global experts beyond traditional networks.

• Encourage participation from underrepresented regions to balance workloads.

2. Structural Process Improvements

• Adopt structured peer review templates to guide reviewers and improve inter-reviewer consistency.

• Implement “publish, review, curate” models with shared review reports across journals to reduce redundant reviewing.

• Apply distributed peer review (DPR) in grants, with safeguards to prevent conflicts of interest.

3. Enhancing Transparency and Accountability

• Expand transparent peer review where reports are published alongside articles.

• Require disclosure of any AI assistance used, including model type, prompts, and date of use.

4. Incentivizing Quality Work

• Recognize peer review in formal research assessments.

• Consider sustainable financial incentives for time-critical reviews in high-stakes contexts.

• Publicly acknowledge high-quality reviewers in journals and conferences.

The Realistic and Responsible Role of AI

AI should augment, not replace, human judgment in peer review. Responsible applications include:

• Pre-screening manuscripts for factual errors, missing references, statistical anomalies, and image manipulations.

• Reviewer support tools that help structure feedback, translate reviews, and check consistency with cited work.

• Workload reduction by automating repetitive checks and surfacing relevant literature.

• Bias detection by analyzing language patterns and flagging potentially discriminatory or overly harsh reviews.

Guardrails for ethical AI use:

• Keep sensitive manuscripts in secure, offline AI environments to avoid IP leakage.

• Mandate transparency on AI use in any stage of review.

• Ensure AI recommendations are always subject to human oversight and final decision-making.

• Regularly audit AI outputs for bias, factual accuracy, and unintended patterns.

Outlook for the Future

If AI adoption is approached responsibly, the next decade could see peer review evolve into a hybrid human–machine process that is:

• Faster: Automated checks could cut initial screening times from weeks to hours.

• More rigorous: AI can assist in fact-checking, statistical validation, and fraud detection at scale.

• More inclusive: AI-enabled reviewer identification can diversify the pool and reduce over-reliance on a small elite.

However, if AI is allowed to dominate without transparency or safeguards, the risks are serious: erosion of trust, homogenization of scholarly discourse, and weakening of the human critical judgment that science depends on.

The realistic future is not AI replacing peer reviewers, but AI becoming an embedded infrastructure that handles the heavy lifting, while human reviewers focus on interpretation, originality, and contextual judgment. Achieving this balance will require not just technology, but cultural change, policy enforcement, and continuous oversight.