Half of Clinical Trial Professionals Cite Trust and Regulation as AI Barriers
Trust and regulatory uncertainty remain the biggest obstacles to artificial intelligence adoption in clinical research, according to a poll of clinical trial professionals conducted at the Clinical Trials Technology Congress in London by the Pistoia Alliance.
Despite these concerns, early signs suggest AI is delivering value. Among respondents, 42 percent reported early return on investment, while another 23 percent expect ROI but haven't yet realized it.
Over the next three to five years, respondents expect AI to have the greatest impact on data cleaning, analysis, and insight generation (48 percent), followed by sourcing and engaging patient cohorts (22 percent).
Regulators Push for Earlier Collaboration
During the congress, regulators from the UK Medicines and Healthcare products Regulatory Agency, the Danish Medicines Agency, and the Swedish Medical Products Agency emphasized the need for pharmaceutical companies to collaborate with regulatory agencies earlier in AI adoption.
The goal is to ensure AI remains safe, compliant, and transparent throughout clinical development. "Speed without control is not enough when patient safety is at stake," said Becky Upton, president of the Pistoia Alliance.
Regulators are increasingly willing to work with industry to formalize guidance around AI governance. The Pistoia Alliance is convening pre-competitive working groups that bring together pharmaceutical companies, technology providers, and regulators to establish common frameworks for compliant AI adoption.
The shift reflects industry movement away from isolated AI pilot projects toward standardized and validated workflows that can withstand regulatory scrutiny.
Patient-Generated Data Gains Ground
Clinical trial professionals are expanding their use of patient-generated data and real-world data sources. Sixty percent of respondents are already using, piloting, or exploring these data-including information gathered through social media listening-to inform clinical development decisions.
Fifty-eight percent of respondents said the primary value of social media listening lies in understanding patient needs, monitoring sentiment, and identifying unmet needs outside traditional trial environments.
Using external data sources can help organizations design trials that better reflect real-world patient experiences. However, managing diverse data streams raises ethical and standardization concerns. The Pistoia Alliance has developed a best-practice framework to guide organizations through the ethical use of social media data in clinical research settings.
Laboratory Data Standards Critical for AI Success
For laboratory managers supporting translational research and clinical trial operations, these trends carry direct implications. As AI adoption expands, laboratories will be expected to generate highly traceable, structured, and standardized data suitable for advanced analytical tools.
To support compliant AI integration, laboratory leaders should strengthen foundational practices:
- Align laboratory information management systems to export structured, high-quality datasets compatible with AI workflows
- Train staff on data integrity and documentation practices necessary for auditable and explainable machine learning models
- Collaborate with bioinformaticians and computational teams early to validate assays and analytical workflows before deployment in clinical trial pipelines
By establishing validated data standards at the laboratory level, organizations can bridge the gap between exploratory AI initiatives and scalable, regulator-ready clinical development programs.
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