AI in Clinical Research: Too Vital to Ignore in an Era of Budget Constraints

In today’s challenging healthcare landscape, all research institutions—from academic medical centers to community hospitals—face mounting financial pressures. The new administration’s proposal to cap NIH indirect cost recovery at just 15% (down from the current rates that often range from 40-60%) represents a potential cut of billions in research infrastructure funding. This dramatic reduction would force institutions to find significant operational efficiencies while somehow maintaining research quality and patient care standards.

Against this backdrop, a troubling trend has emerged: the proliferation of overly restrictive AI prohibition clauses in clinical research agreements. These clauses are not just impractical—they actively work against the efficiency imperative that all healthcare institutions now face, regardless of their size or academic affiliation.

“The question isn’t whether AI will be used in clinical research; it’s how to use it responsibly while protecting legitimate interests.”

The Disconnect Between Reality and Regulation

Clinical research organizations today operate in an environment where AI isn’t just a specialized tool—it’s woven into the fabric of standard business applications. Microsoft 365, Google Workspace, electronic health records, document management systems—all now incorporate AI capabilities by default.

When pharmaceutical companies demand broad language prohibiting the use of confidential information in AI systems, they’re essentially asking the impossible. As one major healthcare institution recently discovered in negotiations with a global pharmaceutical company, such broad prohibitions create significant practical and operational concerns that could require ceasing use of essential tools entirely.

The operational impacts extend far beyond mere inconvenience. Research institutions have noted that clinical research administration increasingly relies on AI-enhanced systems for data management, compliance tracking, and document processing. Overly restrictive language would significantly impair study efficiency and patient enrollment timelines.

Efficiency Is No Longer Optional

The financial pressures facing research institutions demand a new approach. As the response to overhead cuts shows, research institutions will have to find ways to operate more efficiently. The instinctive response may be to sound the alarm about what will be lost, but institutions must also confront the inefficiencies that waste time and money without improving research outcomes.

In this context, AI tools represent one of the most promising pathways to achieving necessary efficiencies. From accelerating document review to improving protocol compliance to streamlining regulatory submissions, AI offers tangible benefits that directly address the administrative bottlenecks plaguing clinical research.

A Path Forward: Targeted Language, Not Blanket Prohibitions

Fortunately, there is a middle ground between unfettered AI use and impractical prohibitions. Leading research institutions are proposing more targeted language that addresses legitimate concerns while acknowledging operational realities.

Instead of blanket prohibitions, more nuanced approaches typically distinguish between:

Prohibiting the use of confidential information for AI model training
Allowing the use of embedded AI in standard business productivity tools
Preserving each party’s right to use their own confidential information as they see fit

This approach illustrates how the industry can address specific risks—unauthorized training of AI models, incorporation into training datasets—while permitting the essential AI-enhanced productivity tools that make clinical research possible in today’s environment.

The Cost of Inaction

Failing to address this issue collaboratively carries significant costs. As research into contract negotiations shows, institutions and pharmaceutical sponsors spend months negotiating Clinical Trial Agreements (CTAs), with delays stemming not from scientific complexity but from administrative processes that have evolved in complexity over decades without systematic review or improvement.

Adding impractical AI restrictions to this already cumbersome process would only exacerbate these inefficiencies—at precisely the moment when research organizations can least afford administrative overhead.

A Call for Industry-Wide Standards

The clinical research community must come together quickly to develop practical, standardized approaches to AI governance in research agreements. Rather than each organization independently fighting the same battles, industry associations, research institutions, and pharmaceutical sponsors should collaborate to establish reasonable guardrails that:

Protect truly sensitive confidential information from misuse in AI training
Allow for the essential use of AI-enhanced productivity tools
Establish clear boundaries around data ownership and usage
Enable organizations to benefit from their own data and intellectual property

Conclusion: Evolution, Not Prohibition

In an era of increased scrutiny on research costs and efficiency, AI tools are not a luxury—they’re a necessity. The question isn’t whether AI will be used in clinical research; it’s how to use it responsibly while protecting legitimate interests.

By adopting balanced, practical approaches to AI governance, the research community can harness these powerful tools to reduce administrative burden, accelerate trial timelines, and ultimately deliver new treatments to patients more efficiently—all critical goals in today’s challenging funding environment.

The path forward isn’t prohibiting technology—it’s providing sensible guardrails that allow innovation to flourish.

Ready to Modernize Your Clinical Trial Agreements?

If your team is grappling with impractical AI clauses, struggling with slow contract cycles, or simply seeking a smarter way to accelerate negotiations, The Contract Network is here to help. Our AI-powered platform is purpose-built to handle today’s complex agreement landscape—including the nuanced realities of AI use in clinical research.

Let’s talk about how TCN can support your institution’s goals.

Schedule a demo or contact us