Our approach to AI

What differentiates Elsevier’s AI?

What differentiates Elsevier’s AI is how we embed trust into every step in the workflow:

• Trusted content and data that reflect the scholarly and clinical record

• Attributable, citable outputs that connect back to original sources

• Human expertise and oversight to support responsible evaluation and use

• Responsible AI governance built around privacy, IP protection, neutrality, transparency and accountability

• Continuous quality evaluation to assess performance, safety and evidence fit over time

What we build into AI research-ready workflows

1. Trusted content foundation

We ground AI experiences in scientific and medical knowledge curated through established scholarly processes. That helps ensure outputs are built on information users can validate and rely on.

2. Evidence you can trace

AI should be inspectable. We prioritize and emphasize:

• Links to original publications

• Attribution that recognizes intellectual contribution

• Citable context so users can verify what the system used and why

3. Domain expertise and human judgment

AI is a productivity accelerant, but decisions still require expertise. We design our solutions to support expert review — so researchers and clinicians can evaluate evidence in context and be more confident in their decisions.

4. Transparency for better critical thinking

Elsevier aims to make outputs more understandable by helping users see:

• What sources were used

• How answers are constructed (where applicable)

• Where uncertainty or limitations exist

5. Responsible AI governance

Trust also depends on how systems are governed. Our approach includes:

• Privacy and security protections

• IP protection

• Neutrality considerations

• Accountability mechanisms aligned with responsible AI principles and standards

How this shows up across research, clinical and education use cases

You get faster progress with governed evidence and human accountability.

For researchers

Accelerate discovery and synthesis while preserving verification:

• Move faster through evidence exploration

• Keep provenance visible for reproducibility and review

For clinicians

Support decisions with traceable, evidence-backed context:

• Connect answers to peer-reviewed literature

• Reinforce confidence at the point of care

For educators and institutions

Enable responsible adoption:

• Support AI literacy and governance readiness

• Clarify how trusted content and oversight improve outcom

Governance that evolves with the ecosystem

As AI changes how people search, read and generate insights, we will continue to strengthen governance with partners and standards-based approaches. That includes transparency expectations, attribution visibility and ongoing evaluation of quality and risk.