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Wiley Partners with Anthropic on AI Integration for Scholarly Research

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Wiley has announced a strategic partnership with Anthropic for a pilot project to integrate academic and scholarly research into the AI for use as source material. The partnership hinges on the adoption of the Model Context Protocol (MCP), an open standard that will enable integration between peer-reviewed content and AI platforms. This initial integration covers multiple academic disciplines, including life sciences, education, and earth sciences and will work to provide participating university partners with enhanced access to Wiley research content.

The partnership focuses on establishing standards for how AI tools can integrate scientific journal content, while maintaining author attribution and citations. As such, information accessed through MCP carries its provenance and maintains full context, ensuring author attribution so researchers and AI applications can trace insights back to their original sources.

“The future of research lies in ensuring that high-quality, peer-reviewed content remains central to AI-powered discovery,” Josh Jarrett, SVP of AI Growth at Wiley, said. “Through this partnership, Wiley is not only setting the standard for how academic publishers integrate trusted scientific content with AI platforms, but is also creating a scalable solution that other institutions and publishers can adopt.”

The collaboration will see immediate application through its integration with Anthropic’s Claude for Education program. Launched on April 3, Claude for Education provides universities with universal access to Claude’s AI tools and further integrate them into the educational experience. The first schools participating are Northeastern University, the London School of Economics, and Champlain College.

“We’re excited to partner with Wiley to explore how AI can accelerate and enhance access to scientific research,” Lauren Collett, who leads higher education partnerships at Anthropic, said about the agreement with Wiley. “This collaboration demonstrates our commitment to building AI that amplifies human thinking—enabling students to access peer-reviewed content with Claude, enhancing learning and discovery while maintaining proper citation standards and academic integrity.”

The initial collaboration represents what Wiley says is a test case for broader AI integration in academic publishing, and Wiley describes it as a pilot program. Wiley plans to make a beta version of the integration more widely available in fall 2025. The goal of this pilot project is to assist Wiley in developing “a blueprint for how AI should integrate with scholarly content.” The intent is to establish best practices that emphasizes respect for copyright and intellectual property, as well as standards for consistent and accurate attribution and citation.

Anthropic is currently being sued by three authors who allege that the AI firm used their copyrighted works without permission to train its AI systems. In June, a court ruled that while using legally acquired copyrighted books to train AI large language models constitutes fair use, downloading pirated copies of those books for permanent storage violates copyright law. Jarrett stressed that it is Wiley’s belief that “AI is here to stay, and should be built on verified, high-quality information. This is where we can add value as a publisher, to ensure that AI models are using trusted sources.”

The new agreement is the latest in a series of partnerships Wiley has made with AI companies. At last year’s Frankfurt Book Fair Wiley announced Wiley AI Partnerships, described as “a co-innovation program” that “aims to develop new AI applications, assistants, and agents in partnership with innovative companies, to empower researchers and practitioners and help drive the pace, efficiency and accuracy of scientific discovery.” It’s first partnership was with Potato, an AI research assistant powered by peer-reviewed literature.

In May, Wiley partnered with Perplexity to integrate Wiley’s educational content into Perplexity’s generative AI search platform for students and educators. Early users of that platform include Texas A&M University and Texas State, as well several universities in the U.K.

Wiley has also moved aggressively in signing licensing agreements with AI companies. It generated AI revenue of $40 million in the fiscal year ended this May, which included $9 million from an agreement with a third firm in a deal worth a total of $18 million.





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New Empirical Research Report on AI-Driven Drug Repurposing

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AI-Driven Drug Repurposing Solutions Market

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AI in health care could save lives and money − but change won’t happen overnight

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Imagine walking into your doctor’s office feeling sick – and rather than flipping through pages of your medical history or running tests that take days, your doctor instantly pulls together data from your health records, genetic profile and wearable devices to help decipher what’s wrong.

This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives.

What’s more, a 2023 study found that if the health care industry significantly increased its use of AI, up to US$360 billion annually could be saved.

But though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.

I’m a professor and researcher who studies AI and health care analytics. I’ll try to explain why AI’s growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI’s widespread adoption by the medical industry.

Inaccurate diagnoses, racial bias

Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.

AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care.

But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn’t perfectly match the patient in front of them.

As a result, AI doesn’t always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations.

Racial and ethnic bias is another issue. If data includes bias because it doesn’t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened.

Humans and AI are beginning to work together at this Florida hospital.

Data-sharing concerns, unrealistic expectations

Health care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor’s offices simply don’t have the time, personnel, money or will to implement AI.

Also, many cutting-edge AI systems operate as opaque “black boxes.” They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification.

But developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but a practical necessity for adoption in health care settings.

There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records.

For instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient’s data. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards.

Privacy concerns also extend to patients’ trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care.

The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.

Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they’re safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.

AI could rapidly accelerate the discovery of new medications.

Incremental change

Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time.

Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help.

Suffice to say that health care’s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI’s potential to treat millions and save trillions awaits.



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Report shows China outpacing the US and EU in AI research

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Governments now face the reality that falling behind in AI capability could have serious geopolitical consequences, warns a new research report.

AI is increasingly viewed as a strategic asset rather than a technological development, and new research suggests China is now leading the global AI race.

A report titled ‘DeepSeek and the New Geopolitics of AI: China’s ascent to research pre-eminence in AI’, authored by Daniel Hook, CEO of Digital Science, highlights how China’s AI research output has grown to surpass that of the US, the EU and the UK combined.

According to data from Dimensions, a primary global research database, China now accounts for over 40% of worldwide citation attention in AI-related studies. Instead of focusing solely on academic output, the report points to China’s dominance in AI-related patents.

In some indicators, China is outpacing the US tenfold in patent filings and company-affiliated research, signalling its capacity to convert academic work into tangible innovation.

Hook’s analysis covers AI research trends from 2000 to 2024, showing global AI publication volumes rising from just under 10,000 papers in 2000 to 60,000 in 2024.

However, China’s influence has steadily expanded since 2018, while the EU and the US have seen relative declines. The UK has largely maintained its position.

Clarivate, another analytics firm, reported similar findings, noting nearly 900,000 AI research papers produced in China in 2024, triple the figure from 2015.

Hook notes that governments increasingly view AI alongside energy or military power as a matter of national security. Instead of treating AI as a neutral technology, there is growing awareness that a lack of AI capability could have serious economic, political and social consequences.

The report suggests that understanding AI’s geopolitical implications has become essential for national policy.

Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!



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