Key Takeaways
• Wiley has partnered with AWS to launch a generative AI agent that enables full-text scientific literature search across its journal content
• The tool, announced at the AWS Life Sciences Symposium, is the first AI agent of its kind from a publisher on the AWS platform
• Integrated with AWS Bedrock, the agent supports complex use cases like biomarker discovery and clinical trial protocol generation
• The AI agent is part of an open-source toolkit aimed at advancing life sciences research through intelligent, agent-driven systems
• Initial access is limited to Creative Commons-licensed content, such as open-access journals including Cancer Medicine
In a milestone collaboration, Wiley has announced the launch of a generative AI agent in partnership with Amazon Web Services (AWS), aimed at enhancing how researchers search and interact with scientific literature.
The tool, revealed on May 6, 2025, at the AWS Life Sciences Symposium in New York, is being introduced as part of an open-source toolkit developed by AWS to support healthcare and life sciences applications.
The AI agent is the first of its kind launched by a publisher on AWS and is intended to provide researchers with a more comprehensive and efficient way to search full-text scientific articles.
By going beyond conventional abstract-level search, the tool unlocks deeper content within research articles—enabling searches through methods, results, and data sections that are often overlooked by traditional discovery systems.
Advancing Research With Full-Text Access
Traditional literature search platforms typically allow researchers to scan abstracts or titles, which often omits critical details needed for complex data synthesis or experimental replication.
Wiley’s AI agent aims to solve this gap by granting AI-powered access to full-text content, beginning with articles published under Creative Commons licenses.
• Allows semantic search across full-text content
• Includes open-access journals like Cancer Medicine
• Accelerates discovery from hours/days to minutes
“Our collaboration with AWS demonstrates how researchers can access and leverage scientific literature to power more effective discovery across the life sciences sector,” said Josh Jarrett, SVP and GM of AI Growth for Wiley. “By integrating Wiley’s authoritative full-text content into AWS’s technology built on Bedrock Agents, we’re showcasing the potential of comprehensive literature search to accelerate innovation and bring critical scientific insights directly into researchers’ existing workflows.”
Built on AWS Bedrock and Open-Source Frameworks
The new AI agent is built using AWS Bedrock Agents, a service that allows developers to build and deploy generative AI-powered systems securely.
As part of AWS’s open-source toolkit, Wiley’s agent offers not just search functionality but a framework for organizations to customize and extend agentic capabilities to suit specialized research tasks.
• Biomarker discovery
• Clinical trial protocol development
• Evidence synthesis for therapeutic research
“We’re excited to work with Wiley to explore how AI-powered agents can enrich evidence-based research with dynamic, detailed, and verifiable scientific content,” said Dan Sheeran, General Manager, Healthcare and Life Sciences, AWS. “The cure for cancer isn’t going to come from an abstract, but will be derived from researchers interrogating and synthesising internal and external data. This collaboration demonstrates how customised AI agents with trusted information sources like Wiley’s research content can enable life sciences researchers to build these more powerful and informed discovery systems.”
Scientific, Technical, and Industry Impact
By enabling AI agents to search entire articles rather than surface-level summaries Wiley and AWS are redefining the possibilities for scientific information retrieval. This could significantly reduce the time required for literature review, hypothesis development, and experimental design.
• Faster access to relevant, detailed research
• Improved ability to extract replicable methodologies and datasets
• Greater integration with clinical and preclinical AI models
The use of trusted, peer-reviewed data also helps ensure that machine-generated insights remain grounded in validated science—an essential consideration for medical and scientific accuracy.
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