Today on Clinical Trials Day, we spotlight the evolving landscape of research with Professor Jennifer Visser-Rogers, chief scientific officer at Coronado Research. As we celebrate advances in patient-centric drug development, rare diseases remain both one of the most urgent and most underserved therapeutic areas. With over 7,000 distinct conditions affecting more than 300 million people worldwide, the need for innovative trial models has never been clearer.

In this Q&A, Professor Visser-Rogers explains why precision medicine, gene editing, and adaptive study designs are poised to revolutionize rare disease trials—and why seamless, standardized data integration will be the golden thread running from protocol design to post-market surveillance.

She also shares her bold prediction that by 2030, AI-driven patient matching and decentralized technologies will transform recruitment, diversity, and real-time monitoring across all therapeutic areas, while keeping “humans in the loop” to ensure ethical, patient-focused outcomes.

Read on for actionable insights on the future of clinical research—and how the industry can accelerate breakthroughs for those who need them most.

What unmet need or therapeutic area do you think is ripe for a clinical trial breakthrough?

One of the most promising and underserved areas ripe for a clinical trial breakthrough is rare diseases. While each rare disease affects a relatively small number of individuals, collectively there are over 7,000 rare diseases that impact more than 300 million people worldwide.^[1]

What’s changing the game is the rapid advancement in genomics, gene editing, and precision medicine. We’re now able to identify the molecular and genetic basis of many rare diseases, enabling targeted therapies designed for very specific patient populations. Gene therapies, in particular, offer the possibility of one-time curative treatments.

However, the clinical trial ecosystem has not fully adapted to these opportunities. Traditional trial designs are often poorly suited for ultra-small patient populations, and many individuals go undiagnosed or are geographically dispersed, making recruitment and retention a major challenge.

Clinical trial designs offer the opportunity to make a significant impact in rare diseases. N-of-1 trials, platform trials, and the use of real-world data as external controls are gaining traction to address small patient populations and accelerate development. Adaptive designs and Bayesian methods are also transforming rare disease clinical trials by enabling more flexible, data-driven decision-making, which reduces patient burden and maximises insights from small, often heterogeneous populations.

With the right incentives, regulatory support, and technological tools, the rare disease space is poised for transformational breakthroughs over the next decade.

What’s your biggest prediction for how clinical trials will change by 2030?

I predict that by 2030, AI and technology will significantly streamline and personalise the clinical trial process. AI will be instrumental in designing smarter protocols, identifying optimal endpoints, and selecting patient populations and trial sites based on real-world data. It will also accelerate patient recruitment by analysing electronic health records and genomic data to match participants to trials more accurately and efficiently. This will be especially impactful in areas like oncology and rare diseases, where finding the right patients quickly is critical.

At the same time, the growing use of digital technologies, such as wearables, remote monitoring tools, and mobile apps, will make decentralised and hybrid trials the norm. These tools will enable real-time data collection, improve patient adherence, and reduce the need for in-person visits, thereby expanding access and improving diversity in clinical research. Overall, AI and technology will help create a more agile, inclusive, and data-rich trial ecosystem by 2030.

The “humans in the loop” will remain essential to ensure AI and technology innovations in clinical trials are applied thoughtfully and ethically. While AI can process vast amounts of data and automate routine tasks, human expertise will be crucial for interpreting complex clinical contexts, making nuanced decisions, and safeguarding patient safety and privacy. Humans will also guide AI systems by setting meaningful objectives, validating outputs, and addressing biases, ensuring that technology complements, rather than replaces, the judgment and empathy required in clinical research.

If you could change one thing about the current trial ecosystem, what would it be?

If I could change one thing about the current trial ecosystem, it would be to establish seamless, standardised data integration across the entire trial lifecycle, treating data as a golden thread that connects every stage from protocol design to post-market surveillance. Right now, data is often siloed, inconsistently formatted, and underutilised, leading to inefficiencies and missed insights, slowing down decision making. A unified, interoperable data infrastructure would enable smarter trial designs, real-time decision-making, and better evidence generation, ultimately accelerating innovation and improving patient outcomes.