The World Health Organization is calling for stronger oversight of artificial intelligence used in health-related research.
On 21 September 2026, WHO highlighted and formally launched its report:
Artificial Intelligence-related Health Research: Ethics Review and Oversight.
The report addresses an increasingly important problem.
Artificial intelligence is already being used to analyse health data, support research, generate hypotheses and develop new technologies.
However, traditional research-ethics frameworks were largely created before modern AI systems became widespread.
WHO warns that existing oversight mechanisms may therefore not fully address risks associated with:
- bias;
- lack of transparency;
- privacy;
- fairness;
- accountability;
- rapid deployment of AI tools.
For pharmaceutical companies, this is highly relevant wherever AI is used in clinical research, health-data analysis or development of evidence that may ultimately support regulatory decisions.
WHO Identifies Three Types of AI-Related Health Research
The report distinguishes three broad categories:
- health-related data science using AI;
- research conducted with AI tools and technologies;
- health-related research on AI tools and technologies.
This distinction is useful because the ethical risks differ depending on how AI is used.
For example, there is a difference between:
using AI to analyse an existing clinical dataset
and
conducting research to evaluate a new AI system that may later affect patient care.
The governance approach should therefore reflect the actual role of AI in the research.
Research Ethics Committees May Need New Capabilities
WHO emphasises that Research Ethics Committees remain central to protecting research participants.
However, AI creates technical and governance questions that conventional ethics committees may not always be equipped to evaluate.
These can include:
- how the model was developed;
- which data were used;
- whether the data are representative;
- how bias is assessed;
- whether outputs are explainable;
- how privacy is protected;
- how model changes are managed.
WHO therefore identifies the need for additional expertise, training and resources.
This is an important message for pharmaceutical sponsors.
An AI-enabled research protocol may require a different level of technical explanation than a conventional protocol.
Ethical Risk Should Be Addressed Early
Another important principle is that ethical review should not become a final administrative check performed after the AI system has already been designed.
Researchers should identify AI-specific risks early.
This may include considering:
- data provenance;
- informed consent;
- privacy;
- population representativeness;
- bias;
- possible downstream harms;
- transparency;
- limitations of the model.
This creates a useful lifecycle sequence:
Research Question → Data Strategy → AI Design → Ethical Risk Assessment → Study Execution → Monitoring → Publication / Implementation
Ethical considerations therefore become part of AI design rather than an external review performed only at the end.
Data Governance Is Central
For pharmaceutical research, one of the most important implications concerns data governance.
AI may depend on very large datasets.
These datasets may come from:
- clinical trials;
- electronic health records;
- registries;
- real-world data;
- biobanks;
- genomic databases.
The fact that data are technically accessible does not automatically mean that every AI use is ethically appropriate.
Relevant questions include:
- Was the original consent compatible with the new use?
- Can individuals or groups be re-identified?
- Are some populations under-represented?
- Could the model create systematic disadvantage?
- Who benefits from the resulting AI system?
For pharmaceutical companies, these questions increasingly intersect with data protection, clinical governance and regulatory evidence generation.
Bias Is Not Only a Technical Performance Problem
Bias is often discussed as an AI-performance issue.
WHO treats it also as an ethical issue.
A model can appear accurate overall while performing poorly for particular groups.
This may occur because:
- training data are unbalanced;
- certain populations are under-represented;
- historical data contain structural bias;
- the model is deployed outside its original Context of Use.
For pharmaceutical development, this can be particularly important where AI influences:
- patient selection;
- risk stratification;
- endpoint analysis;
- safety assessment;
- evidence generation.
A single average performance metric may therefore be insufficient.
Oversight Cannot Be Left to the Ethics Committee Alone
WHO also makes clear that responsible AI research requires more than Research Ethics Committees.
Other important actors include:
- research funders;
- data-access bodies;
- scientific journals;
- professional societies;
- regulatory agencies.
This is significant because some AI risks are systemic.
They cannot be solved by reviewing each individual study independently.
For example, common problems involving data access, model transparency or reproducibility may require broader institutional or regulatory solutions.
Why This Matters for Pharmaceutical R&D
The report is not pharmaceutical GMP guidance.
However, it is directly relevant to pharmaceutical R&D organisations increasingly using AI in:
- clinical-development analytics;
- real-world evidence;
- biomarker research;
- patient stratification;
- safety analysis;
- computational biology.
As AI-generated analysis becomes more important to evidence generation, companies will need to demonstrate not only that models perform technically, but also that their use is ethically justified and appropriately governed.
The assurance model therefore becomes broader:
Scientific Validity + Data Governance + Ethical Oversight + Transparency + Human Accountability
From Technical Validation to Responsible Research Governance
The WHO report reinforces a wider trend in AI regulation.
AI assurance is moving beyond technical validation alone.
A model may be technically accurate but still create problems if:
- the data were obtained or reused inappropriately;
- important groups are under-represented;
- the model’s limitations are hidden;
- responsibility for decisions is unclear;
- participants are exposed to risks they did not reasonably anticipate.
Responsible AI therefore requires a broader governance system.
Why This Development Is Important
Pharmaceutical companies are investing heavily in AI-enabled research.
The WHO report provides an important reminder that faster analysis does not reduce ethical responsibility.
As AI becomes more deeply integrated into health research, the governance question is shifting from:
“Can AI perform this analysis?”
towards:
“Is this AI-supported research scientifically valid, ethically acceptable, transparent and appropriately governed?”
That distinction is likely to become increasingly important as AI-generated evidence moves closer to regulatory and clinical decision-making.
Regulatory Status Note
The WHO report is guidance on ethics review and oversight of AI-related health research.
It is not GMP guidance and does not create new legally binding pharmaceutical requirements.
Its relevance to pharmaceutical companies is primarily in clinical research, health-data science, AI-supported evidence generation and research governance.
WHO describes the publication as a starting point for strengthening future standards, guidance and governance approaches in AI-related health research.
Sources
WHO – New report calls for stronger ethics oversight of AI-related health research, 21 September 2026:
https://www.who.int/ … ated-health-research
WHO – Artificial Intelligence-related Health Research: Ethics Review and Oversight: