Wiley AI solutions

AI is transforming clinical care. The quality of your evidence determines the quality of your outcomes.

Healthcare AI operates under different constraints than consumer AI. Here's what that means for your organization.

AI adoption in healthcare is accelerating, but so is physician caution

81% of physicians now use AI in a professional context, double the share from 2023. And 76% say AI tools give them an advantage in their ability to care for patients, up from 65% in 2023. Yet 40% remain equally excited and concerned about AI's role in healthcare. Top concerns center on protecting patient privacy and preserving the integrity of the patient-physician relationship. 88% say validation of safety and efficacy is important in facilitating AI adoption. For many, the question isn't whether to use AI. It's whether the evidence behind it can be trusted. That's where the quality of your content source matters most.

In healthcare, "good enough" data isn't good enough

In consumer AI, an 80% accurate recommendation engine still delivers value. A follow-up query can fill the gap. Healthcare AI works differently. A missed medication interaction isn't a minor inconvenience. A diagnostic suggestion based on incomplete evidence isn't merely suboptimal. In clinical settings, the consequences of getting it wrong are real. That demands a standard of data quality that open-web sources were never built to meet.

Abstracts give your AI a fraction of the available evidence

Open-web abstracts are a summary of the paper. Methods, data, and discussion sit behind them, and leading research is often paywalled and missing from open sources entirely. A proof of concept between Wiley and Biorelate found that analyzing full-text content yielded 82% more causal relationships than reading abstracts and titles alone. Your AI is only as good as what it reads.

Workflow integration is the single biggest determinant of adoption

Market intelligence from HIMSS 2026 confirms what health systems are experiencing: ambient listening and documentation is still the most widely deployed and validated AI use case. Clinical decision support is the next layer being added on top, not a standalone category replacing it. Workflow integration is the single biggest determinant of adoption. Clinicians don't want another tool, or to lose patient context. According to the AMA 2026 Physician Survey, 55% of physicians prefer to be consulted on AI adoption decisions, and 30% want direct responsibility for implementation. The tools that earn adoption are the ones that fit inside existing workflows, with evidence clinicians can stand behind.

In regulated environments, three things are non-negotiable

For clinical AI to earn the trust of clinicians, patients, and regulators, it must meet three standards:

Accuracy: AI claims hold up under scientific scrutiny.
Provenance: your teams can see where every output came from.
Citation traceability: AI assertions can be traced back to source, defensibly.

Getting all three right starts with choosing the right content source.

Physician adoption and attitude statistics: AMA 2026 Physician Survey on Augmented Intelligence, March 2026. Market intelligence: HIMSS 2026. Full-text vs. abstracts: Wiley/Biorelate proof of concept.

Prefer to choose for yourself?

Each option delivers Wiley's trusted clinical evidence, but the content and integration method differ. Use the table below to find the right fit for your infrastructure, team, and existing licenses.

	Wiley Agent Knowledge Base: Cochrane Library		Microsoft Dragon Copilot integration	Nexus Domains: Health Sciences & Healthcare
	Through AWS	Direct from Wiley
Best for	Organizations building or running their own AI applications who want to purchase access through AWS Marketplace and draw against existing AWS commitment spend	Organizations with their own AI application or clinical AI solution who want to integrate Cochrane evidence directly, with maximum flexibility over how it is consumed and deployed	Health systems already using or piloting Microsoft Dragon Copilot for ambient listening and clinical documentation	Organizations whose AI applications need broad, full-text health sciences literature across specialties and society publishers
Content	11,000+ systematic reviews and 3,500+ Cochrane Clinical Answers	11,000+ systematic reviews and 3,500+ Cochrane Clinical Answers	11,000+ systematic reviews and 3,500+ Cochrane Clinical Answers	1.9M+ full-text publications across 850+ peer-reviewed journals across major therapeutic areas
What's delivered	Real-time API access to 11,000+ Cochrane systematic reviews and 3,500+ Cochrane Clinical Answers. Semantic search, summarization, and insight extraction across the full Cochrane Library. Integrate directly into your AI ecosystem or other development environment.	Real-time API or XML delivery of 11,000+ Cochrane systematic reviews and 3,500+ Cochrane Clinical Answers directly into your AI application of choice. Maximum flexibility over how evidence is consumed and deployed to suit your needs.	11,000+ Cochrane systematic reviews and 3,500+ Cochrane Clinical Answers delivered natively into Microsoft Dragon Copilot. Relevant content is surfaced automatically based on the clinical encounter, with no separate search, no additional login, and no context switching.	AI-ready, full-text peer-reviewed journal content delivered via a single API or raw XML. Curated corpus across major therapeutic areas, with a clear AI licensing framework and citation metadata embedded throughout.
Key advantage	Purchase through AWS Marketplace and draw against your AWS commitment spend, if applicable	Integrate Cochrane evidence into any AI application or clinical AI solution, on your terms and on your timeline	Evidence appears natively within Dragon Copilot, so clinicians access trusted research without leaving their existing workflow	Coverage across cardiology, oncology, neurology, mental health, nursing, and more, from Wiley and leading society publishers including the American Cancer Society, Alzheimer's Association, and Royal College of Obstetricians and Gynaecologists
Clinical AI integration	✓	✓	✓	✓
Full-text access	✓	✓	✓	✓
Citation traceability	✓	✓	✓	✓
Clear AI licensing framework	✓	✓	✓	✓
No IT build required	—	—	✓	—
Multi-specialty journal coverage	—	—	—	✓
AWS Marketplace purchase	✓	—	—	—

New to AI in clinical settings?

Start here. These short guides explain the essentials — no technical background needed.

When an AI application needs to answer a clinical question, it doesn't just draw on what it was originally trained on. Modern clinical AI systems commonly use a technique called Retrieval-Augmented Generation (RAG): when a question comes in, the AI pulls relevant passages from a connected content source in real time, uses them to ground its answer, and returns a response with a citation back to the source. Peer-reviewed journals and systematic reviews can be connected directly to your AI application via API, so every answer it generates is backed by literature your clinicians already trust, with a clear audit trail showing exactly where it came from. RAG is the most common method for real-time clinical applications, though content can also be used for fine-tuning or literature review, as explained in the next section.

Most organizations that already subscribe to our content have a license that covers read access — clinicians and researchers accessing the content directly. Using that same content inside an AI application is a different type of use, and it requires a separate Wiley AI Rights license. There are three tiers, each covering a different way AI can use content. Literature Review covers AI tools that surface and synthesize research. Retrieval-Augmented Generation (RAG) covers AI applications that pull content passages in real time to ground their responses. Fine-Tuning covers using content to train or adapt a model itself, the highest-value and most restricted tier. If you're not sure which tier applies to your use case, our team can help you work it out.

An AI application is shaped by what it reads. A model connected to low-quality, incomplete, or unverified sources will produce outputs that reflect those limitations, and in a clinical setting that has real consequences. Hallucination failures in clinical AI systems have been documented to include fabricated citations, incorrect treatment statements, and inaccurate summaries of patient context. Cochrane systematic reviews and Wiley's peer-reviewed journals go through rigorous editorial and peer review processes before publication. Connecting your AI to that content doesn't eliminate the need for clinical oversight, but it gives clinicians something they can verify.

A large language model (LLM) is a type of AI trained on vast amounts of text. It learns patterns in language well enough to generate coherent, contextually relevant responses to almost any question. That capability is what makes LLMs useful in clinical settings: summarizing research, drafting documentation, surfacing relevant evidence at the point of care. But LLMs have a well-documented limitation: they can produce outputs that sound authoritative but are factually wrong, a problem known as hallucination. In healthcare, hallucinated outputs can include invented drug interactions, fabricated citations, or incorrect clinical summaries. Connecting an LLM to verified, peer-reviewed content, whether through RAG, fine-tuning, or another licensed integration, significantly reduces that risk. It doesn't remove the need for clinical judgment, but it means the AI is working from a foundation that can be checked and traced back to source.

Doctor reviewing information on a tablet in a hospital corridor

Wednesday November 4, 2026 4pm UTC

Publishing

From Clinical Insight to Publication: A Guide for Healthcare Professionals

Turn what you see in practice into published work. Learn how to shape clinical insight into a publication, from first idea to submission.

Thursday November 12, 2026 7pm UTC

Clinical AI

AI in Healthcare: From Practice to Policy

See how AI moves from the clinic to the policy table. Explore what's working in practice today and what it means for governance and patient care.

Tuesday November 17, 2026 11am UTC

Clinical AI

AI Through a Clinician’s Eyes: Opportunities, Challenges & What Lies Ahead

Get a clinician’s view on where AI helps today, where the challenges remain, and what’s coming next for patient care.

Talk to our team

Whether you're ready to move forward or just starting to explore your options, we're happy to help you find the right fit.

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The evidence base you trust for clinical AI