Artificial intelligence is rapidly transforming clinical research, promising faster recruitment, more efficient data analysis, and even automated trial monitoring. Supporters say it could revolutionise how new treatments are developed and approved. But there is a crucial question that often gets overlooked...
Will AI solve the long-standing problem of bias in clinical trials, or could it make it worse?
The Problem AI Needs to Solve
For decades, clinical trials have underrepresented key groups: women, ethnic minorities, older adults, and people with disabilities. This lack of diversity means trial results often don’t fully reflect how treatments work for the wider population.
The consequences can be serious. If a new drug is tested mainly on younger men, there’s no guarantee it will be equally safe or effective for older women. If a medical device is trialled on one ethnic group, it may perform differently — or less effectively — for others.
AI could help address these issues by identifying underrepresented groups in real time, analysing population health data, and streamlining recruitment efforts to ensure trials better reflect the communities they aim to serve.
The Risk of Making Bias Worse
However, AI is only as good as the data it is trained on. If historical clinical trial data is biased — and much of it is — then AI systems may simply replicate those biases at scale.
For example, an AI recruitment tool trained on past participant profiles may “learn” to favour groups that were historically overrepresented, such as middle-aged white men. Automated eligibility screening tools could unintentionally filter out people based on socioeconomic factors, geographic location, or even linguistic differences.
In other words, without careful design and oversight, AI could make clinical trials faster — but not fairer.
Where AI Can Genuinely Help
AI does have significant potential to improve equality, diversity, and inclusion in research. Properly implemented, it could:
Analyse real-time demographic data to flag gaps in trial participation.
Suggest targeted outreach to communities that are underrepresented.
Translate participant information sheets into multiple languages instantly.
Identify logistical barriers, such as transportation or appointment scheduling issues, and suggest solutions.
But this only works if the algorithms are built with inclusivity in mind from the very beginning — and if there’s human oversight to check that decisions are fair and representative.
Why Human Oversight is Non-Negotiable
AI can process vast amounts of data, but it cannot understand the human and cultural contexts that often explain why certain groups are missing from research. That’s where expert review and inclusive trial design come in.
Organisations like inclusive clinical trial specialists based in the UK are already working with research teams to ensure trial materials are accessible, barriers to participation are identified early, and recruitment strategies actively engage underrepresented groups. Combining this human expertise with AI’s analytical power could be the key to solving trial bias — for good.
A Balanced Future
The future of clinical research will almost certainly involve AI. But whether it becomes a tool for fairness or a driver of deeper inequality will depend on the values and priorities of the people designing and deploying it.
The challenge for the healthcare industry is to ensure that AI is used not just to speed up trials, but to make them more inclusive, more representative, and ultimately more reliable. After all, a treatment can only truly be called safe and effective when it works for everyone.
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