A new global reviewer survey from IOP Publishing (IOPP) reveals a growing divide in attitudes among reviewers in the physical sciences regarding the use of generative AI in peer review. The study follows a similar survey conducted last year showing that while some researchers are beginning to embrace AI tools, others remain concerned about the potential negative impact, particularly when AI is used to assess their own work.

Currently, IOPP does not allow the use of AI in peer review as generative models cannot meet the ethical, legal, and scholarly standards required. However, there is growing recognition of AI’s potential to support, rather than replace, the peer review process.

Key Findings:

• 41% of respondents now believe generative AI will have a positive impact on peer review (up 12% from 2024), while 37% see it as negative (up 2%). Only 22% are neutral or unsure—down from 36% last year—indicating growing polarisation in views.

• 32% of researchers have already used AI tools to support them with their reviews.

• 57% would be unhappy if a reviewer used generative AI to write a peer review report on a manuscript they had co-authored and 42% would be unhappy if AI were used to augment a peer review report.

• 42% believe they could accurately detect an AI-written peer review report on a manuscript they had co-authored.

Women tend to feel less positive about the potential of AI compared with men, suggesting a gendered difference in the usefulness of AI in peer review. Meanwhile, more junior researchers appear more optimistic about the benefits of AI, compared to their more senior colleagues who express greater scepticism.

When it comes to reviewer behaviour and expectations, 32% of respondents reported using AI tools to support them during the peer review process in some form. Notably, over half (53%) of those using AI said they apply it in more than one way. The most common use (21%) was for editing grammar and improving the flow of text and 13% said they use AI tools to summarise or digest articles under review, raising serious concerns around confidentiality and data privacy. A small minority (2%) admitted to uploading entire manuscripts into AI chatbots asking it to generate a review on their behalf.

Interestingly, 42% of researchers believe they could accurately detect an AI-written peer review report on a manuscript they had co-authored.

“These findings highlight the need for clearer community standards and transparency around the use of generative AI in scholarly publishing. As the technology continues to evolve, so too must the frameworks that support ethical and trustworthy peer review”, said Laura Feetham-Walker, Reviewer Engagement Manager at IOP Publishing and lead author of the study.

“One potential solution is to develop AI tools that are integrated directly into peer review systems, offering support to reviewers and editors without compromising security or research integrity. These tools should be designed to support, rather than replace, human judgment. If implemented effectively, such tools would not only address ethical concerns but also mitigate risks around confidentiality and data privacy; particularly the issue of reviewers uploading manuscripts to third-party generative AI platforms,” adds Feetham-Walker.

What You Should Know: 

– Mount Sinai Fuster Heart Hospital has announced the launch of The Samuel Fineman Cardiac Catheterization Artificial Intelligence (AI) Research Lab. The new AI lab will use the hospital’s renowned Cardiac Catheterization Lab to advance interventional cardiology and enhance patient care and outcomes.

– Dr. Annapoorna Kini will serve as the Director of the new AI lab. She also directs The Mount Sinai Hospital’s Cardiac Catheterization Lab, which is internationally recognized for its exceptional safety and expertise in complex cases.

Catheterization AI Research Lab Focus

The new lab will focus on many aspects of interventional cardiology, from procedural to educational. Through internal and external collaborations, the lab will explore existing data to gain insights that can significantly impact how healthcare is delivered. AI has the capability to spur new levels of innovation in areas like risk stratification, case planning, and optimizing outcomes.

“While AI is not a magic solution to every problem, there are many places it can make a notable improvement over traditional techniques or bring some approaches that were never possible within reach. In five or so years, we think that many workflows can be augmented by AI to better focus our resources where they are most needed,” says Dr. Kini.

The Samuel Fineman Cardiac Catheterization Artificial Intelligence Research Lab was established in memory of Samuel Fineman, who passed away in 2021. His generous gift was a show of appreciation for the care he received from Dr. Samin K. Sharma.

Pascal AI Labs, a rapidly growing technology company focused on transforming how investment research is conducted, has announced the close of a $3.1 million seed funding round. The funding was led by Kalaari Capital, with additional participation from Norwest, Infoedge Ventures, Antler, and several prominent angel investors.

This funding marks a significant step in the company’s journey to bring advanced, AI-driven research capabilities to financial institutions worldwide.

The new capital will be used to speed up the development of Pascal AI’s autonomous investment workflows, expand its presence in the United States, and form strategic partnerships with key data providers.

The company’s platform is already in use by more than 25 financial firms across the U.S. and the Asia-Pacific region, including private equity funds managing $2 billion in assets and one of the world’s top three asset managers with over $1 trillion under management.

Pascal AI offers secure and native connections to data on over 16,000 publicly traded companies across 27 markets, giving investment teams a broad and reliable foundation for their work.

The problem that Pascal AI is addressing is one that many investment professionals are familiar with. Analysts and portfolio managers are inundated with vast amounts of data from company filings, earnings call transcripts, market reports, and internal research notes.

While existing platforms can surface this information, they often fail to capture the accumulated judgment and institutional knowledge that experienced investors rely on. As a result, analysts spend hours manually piecing together information, and chief investment officers often lack a clear, forward-looking view of their portfolios.

Pascal AI takes a different approach by automating the entire investment lifecycle. The platform learns from a firm’s proprietary history—its past decisions, research notes, and investment patterns so it can reason and act like a seasoned investor rather than simply retrieving data. This means it can proactively connect insights, identify risks, and suggest actions in a way that reflects the unique thinking of each firm.

Because the stakes in investment decision-making are high, trust and security are central to Pascal AI’s design. The platform is built on a proprietary Knowledge Graph that makes every action fully auditable and traceable. It supports enterprise-grade security features, including role-based permissions and the option for on-premise deployment, ensuring that sensitive information remains protected while still enabling robust AI-driven analysis.

Pascal AI was founded by Vibhav Viswanathan and Mithun Madhusudan, both of whom bring deep expertise in finance, artificial intelligence, and scaling technology products.

Viswanathan, a graduate of the University of Chicago Booth School of Business, previously led AWS Inferentia and Neuron in Silicon Valley and has hands-on investment experience from his time at Capital Group and NEA-IUVP.

Madhusudan, an alumnus of the Indian Institute of Management Bangalore, has led AI and product teams at Indian tech unicorns Apna and ShareChat, where he helped scale AI products to more than 100 million users.

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