Choosing the right AI vendor for clinical trials in a rapidly evolving landscape

The AI revolution is here-are you ready to join?

Artificial intelligence is reshaping clinical research today - it's no longer a future concept. For ophthalmology sponsors, the promise is clear: faster clinical trials, smarter insights, and more patient-centric outcomes. But with a flood of healthcare AI vendors making bold claims and offering complex solutions, the path forward can feel anything but clear.

Choosing the right AI vendor for clinical trials isn't just a technical decision - it's a strategic one. The wrong choice can slow you down, introduce risk, waste limited resources, or leave you with a tool that doesn't deliver in the real world. That's why Noelle Saldana, Head of Research Analytics (AI & ML) and Brian Guthrie, Director of Strategic Delivery & Growth in Ophthalmology have distilled the noise into eight essential questions every biotech leader should ask before selecting a healthcare AI vendor:

1. Do they understand the science and your specialty?

The most actionable AI models are built with deep subject matter expertise. Model accuracy means nothing if the variables used aren't relevant to your specific challenges and clinical indication.

• Look for ophthalmology domain expertise that understands your unique endpoints and patient populations.
• Seek vendors who speak your language - not just tech jargon, but the clinical nuances that matter in your trials.

2. How innovative is the solution and what real value does it bring?

Not every shiny new feature deserves the "AI" label, and not every problem needs an AI solution.

• Distinguish between genuine AI that is predictive/prescriptive and dressed-up business intelligence focused on static data & insights - know which one you actually need.
• Be wary of trendy add-ons like chatbots that don't address your core pain points.

3. Can they prove real-world impact, not just potential?

Case studies beat capabilities every time. You need evidence, not promises.

• Ask for specific metrics that measure success in those case studies: what was used before, what motivated the change, and how outcomes improved.
• Focus on outcomes: speed and accuracy in delivering results to sites for patient recruitment and trial execution.

4. How transparent is their AI?

Avoid black-box solutions whose internal workings aren't easily interpretable, even by experts.

• Demand explainability: Look for solutions where users can trace how inputs lead to outputs, model logic is documented and understandable, and they have included a human-in-the-loop to review, validate, and challenge results.
• Ask for transparency: in both the data being leveraged and the algorithms/methods being used. Is their technology dependent on other 3rd party services or open source tools, and are those technologies also transparent?
• How often does the AI get tested & updated? When updates happen, how are they communicated to users/customers? Look for explainability, auditability, and regulatory readiness.

5. What's their integration game plan?

The best AI solution has limited value if it can't work with your existing systems or ecosystem.

• Test compatibility: Can their solution plug into your existing tech stack and workflows? If the solution requires your existing data, how will that data be put into the system? How will it be used and stored?
• Understand implementation requirements-can you use it as-is, or does it need heavy customization?
• Clarify ongoing support needs and data handling protocols.

6. Who's behind the algorithm?

The team building your AI solution matters as much as the technology itself.

• Look for cross-functional expertise: data scientists, AI/ML engineers, clinicians, and regulatory experts working together.
• Understand the team and depth of experience.

7. How do they manage risk and compliance?

AI in clinical trials must meet GxP, GDPR, and FDA expectations. No exceptions.

• Verify regulatory readiness through documented validation processes and audit trails.
• Understand their data privacy protocols and compliance audit trails.

8. Will they be a collaborator - or just a provider?

• Look for a long-term collaborator, not a one-off vendor. Working with a collaborator allows for growth by both organizations benefiting both sides. AI is rapidly evolving and a one-off vendor may not focus on delivering improvements to you.

Conclusion: Choose clarity over complexity

In a landscape crowded by clinical AI solution promises, asking the right questions can be your greatest advantage. By focusing on real-world impact, technology and AI transparency, and proven expertise, you can select an AI vendor who will help you deliver smarter, faster, and more patient-centric trials.

At Fortrea, we combine deep ophthalmology expertise with sharp understanding of the evolving AI landscape. We research, evaluate, and build relationships with emerging and established AI vendors so you don't have to. Our team knows what works in practice, not just theory, and we're here to help you bridge the gap between innovation and implementation.

Let us be your trusted innovation navigator in clinical AI solutions - contact us.