Artificial Intelligence (AI) is no longer just a buzzword in clinical trials; it's shaping the future of research.
Yet, while larger players, especially pharmaceuticals companies, are diving headfirst into AI adoption, smaller trial organizations (sites, CROs, etc.) are hanging back, wary of the risks, complexities, and overall decision and purchasing process.
Beyond the industry conversations, the AI gap between sponsors and sites is widening, driven by differences in resources, data privacy concerns, and the tangled web of regulations.
Sponsors Lead, Sites Hesitate
Pharmaceutical giants like Sanofi, AstraZeneca, and Eli Lilly are at the forefront, harnessing AI to speed up trials, enhance data accuracy, and predict outcomes. They have the advantage of secure, scalable environments like AWS and Azure, paired with validated platforms. The end result: Streamlined operations and faster insights without sacrificing data security.
“Our ambition is to become the first pharma company powered by artificial intelligence at scale, giving our people tools and technologies that focus on insights and allow them to make better everyday decisions. The use of artificial intelligence and data science already support our teams’ efforts in areas such as accelerating drug discovery, enhanced clinical trial design, and improving manufacturing and supply of medicines and vaccines. We have just scratched the surface as to how we embrace these disruptive technologies to achieve our ambition of transforming the practice of medicine.” - Paul Hudson (CEO, Sanofi - in 2023)
And here’s the irony: while smaller clinical trial operations fear sponsor pushback for adopting AI, it’s the sponsors themselves who are leading the charge. The concern is rooted, partly, in a disconnect. Sites worry about liability, but sponsors are increasingly embracing AI as a competitive advantage.
According to a recent report by GlobalData "2024: record year for AI trials", demonstrating the continued growth of AI use in the industry and that AI use, when managed correctly, also aligns with compliance standards.
But smaller clinical trial sites are understandably hesitant. Without the same robust infrastructure, they worry about data privacy breaches, especially when using - or even just looking at - popular but unregulated AI tools like ChatGPT.
A study published by WCG found that among the top challenges for sites, "technology" went from 10% to 18% in just one year (2022 to 2023) and most still lack IT capabilities to securely implement AI. Understandably, many fear repercussions from sponsors if AI usage backfires.
Navigating Security and Compliance
Sponsors are playing it smart by integrating rigorous security measures:
Single-tenant cloud environments: Keeping data siloed and secure.
Zero-retention endpoints: Guaranteeing that no data is stored or repurposed.
Audit trails and human validation: Ensuring every AI-driven decision is traceable and compliant.
For smaller sites, these measures may feel out of reach - either too costly or technically complex. The result? A reluctance to innovate, even when the benefits are clear. And, beyond technology, a large number of small sites, when asked about AI, cite sponsor disapproval (factual or potential) as a major barrier to AI adoption.
Regulatory Guardrails Are Coming
Governments aren’t ignoring this dilemma. The FDA’s draft guidance (2025) and the EU AI Act (rolling out in 2026) aim to address core issues: transparency, data governance, and human oversight. These regulations are expected to require lifecycle documentation, bias testing, and consistent auditability - treating AI solutions as rigorously as any validated system.
The reality is, AI adoption by major pharmaceutical companies operating in Europe shows that it’s possible to leverage AI while staying compliant. Sanofi, for instance, has implemented AI-driven data analysis pipelines while adhering to stringent GDPR requirements.
This highlights that the issue isn’t AI itself but how it is handled and integrated - secure environments and proper governance make it feasible.
Sponsor partnering needs
Sponsors at the recent Global Vaccine Congress communicated that they in fact want sites and CRO's to implement AI and innovation to be more efficient and speed up the clinical trials.
Making AI Work for Everyone
Bridging the adoption gap, however, will take more than wishful thinking. Here’s what’s needed:
Guidance and Education: Clear, accessible frameworks that demystify safe AI practices for smaller sites.
Infrastructure Support: Leveraging cloud credits or sponsor-provided secure environments.
Collaborative Standards: Sponsors sharing AI integration best practices rather than leaving sites to fend for themselves.
Human Oversight: Ensuring that AI-driven decisions are always validated by qualified staff, maintaining the human touch in healthcare (and the "human-in-the-loop" safeguard we, at Clinials, have baked in from the start).
Practical Steps for Smaller Organizations to Leverage AI
The good news is that smaller operations, sites, CROs, and more, don’t have to build AI solutions from scratch.
Platforms like Clinials are already designed with their needs in mind - integrating security features like single-tenant environments or zero-retention right out of the box.
This means that smaller clinical operations can adopt AI confidently, knowing that the heavy lifting on compliance and data protection has been taken care of.
Other solutions, from data vaults to CTMS increasingly offer secure data and document management with integrated AI tools, while cloud providers like AWS and Azure offer specialized healthcare environments that smaller sites can access through grants or sponsorships.
The key is to seek out solutions that have built-in compliance and to leverage platforms that are purposefully designed for healthcare, rather than attempting to repurpose consumer-grade AI tools.
But how do you actually get started? Step by step, in accordance with your needs. And while we can not really recommend one solution above the many others, you can start by checking out our "starter pack":
AI Starter Pack for Clinical Trials
1. Across Phases (early, recruitment, and communication automation)
Clinials: A content generation and simplification platform that supports communication, trial planning, and patient recruitment and clear communication, with built-in compliance. Perfect for smaller sites looking to save time and start operating straight away without complex setup.
https://clinials.com
2. Early Stage (Startup and Feasibility):
Deep 6 AI: Ideal for finding eligible patients by mining unstructured data. Best for sites looking to enhance patient identification with minimal setup.
https://deep6.aiTriNetX: Great for protocol design, offering real-time data insights. Consider this if you’re aiming to optimize feasibility studies.
https://trinetx.com
3. Patient Recruitment:
Grove AI: Uses Grace, an AI assistant, to automate recruitment while maintaining patient engagement. Particularly suited for sites handling multiple trials simultaneously.
https://www.grovetrials.comAntidote Match: Simplifies recruitment by connecting with a wide patient base. Great for public-facing studies.
https://www.antidote.meParadigm: Integrates with EHRs to find candidates right at diagnosis, ideal for oncology trials.
https://www.paradigm.inc/
4. Trial Design:
Saama Life Science Analytics Cloud: Provides predictive analytics for trial efficiency. Look for sponsor partnerships to offset costs.
https://www.saama.com
5. Trial Management:
Medidata Rave EDC: Trusted by big pharma but accessible to smaller sites through flexible licensing. Ensures compliance with robust data capture.
https://www.medidata.com/en/clinical-trial-products/clinical-data-management/edc-systemsCastor EDC: Cost-effective and user-friendly, ideal for decentralized or hybrid trials.
https://www.castoredc.com
6. Post-Trial Data Analysis:
CureMetrix AI: Excellent for imaging data analysis, particularly for radiology-heavy studies.
https://curemetrix.comnference: Leverages AI to mine scientific literature for insights, perfect for evidence generation post-trial.
https://nference.com
Practical Tips for Adoption
Start Small: Test a tool in a single phase before fully committing.
Look for Free Trials: Many platforms offer initial access at no cost, so you can evaluate the fit without financial risk.
Seek Community Insights: Platforms like SCRS (https://myscrs.org) and ClinicalTrials.gov (https://clinicaltrials.gov) regularly share case studies and tool comparisons.
Stay Updated: Attend healthcare innovation webinars from Deloitte and McKinsey (for example) and from industry groups, to learn from peers already implementing these technologies.
The key takeaway? Instead of feeling overwhelmed by the AI landscape, focus on small, manageable steps. Start by exploring the most accessible tools and gradually integrate more advanced solutions as you gain confidence.
The future of clinical trials doesn’t just belong to the big players. With the right support and solutions smaller sites can catch up and thrive.
