Knowledge Center

Artificial intelligence continues to influence nearly every industry, and life sciences are no exception. In a recent conversation on the AI and Business podcast, Damien Nero, Head of Data Science in US Medical at Takeda Pharmaceuticals, shared his perspective on how AI is changing the clinical trial landscape. With over 15 years of experience applying machine learning and real-world data to drug development, Nero outlined both the progress already being made and the challenges that still stand in the way of broader transformation. His insights highlight how pharmaceutical leaders can think strategically about deploying AI to balance innovation with operational efficiency.

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“For almost a decade, it’s taken nearly eight months on average to get from site identification to study startup completion, when all sites are initiated and ready to enroll patients.”

While it’s often hard to quantify the average time spent on clinical trial activities like study startup, this 2018 quote from Tufts Research author Mary Jo Lamberti showcases the very real problem regarding the time it takes to reach key trial milestones.

In fact, in the seven years since that quote, the clinical trial landscape has only become more complex, with clinical trial cycle times increasing despite technology advances. Cycle times are defined as the total duration from the approval of a clinical trial protocol to the database lock (DBL). As reported by Statista, the average clinical trial cycle from 2020 to 2024 increased by seven months.

Additionally, IQVIA recently released research indicating that almost 50% of drug development time is attributed to non-scientific delays, aka operational bottlenecks that create unnecessary gaps between critical milestones.

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“This year, we’ve decided to stick to paper. We know what we’re getting and we’ve always done it this way.”

That was the unwavering response from a clinical operations lead at a pioneering biotech when asked why they still relied on paper diaries for patient-reported outcomes. 

Despite the growing complexity of global trials, the promise of real-time data, and the surge of digital capabilities available today, some organizations have held tight to a method of clinical trial conduct that’s increasingly as outclassed as it is outdated. 

It’s well known that individuals and organizations believe that change can be daunting, stressful, and difficult, especially when the old way is familiar and entrenched. However, much like anything else, having a partner who can help guide you through the process is massively important. But, how do you find the right partner for your trials?

Before locking in a vendor, organizations must engage in a deliberate process to assess their needs, align stakeholders, and set the foundation for long-term success. This blog explores the critical steps sponsors should take before selecting a digital partner, using insights and frameworks drawn from Medable’s therapeutic area standards and industry best practices.

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Learn how Medable powered a decade-long, global long-term follow-up (LTFU) obesity study, achieving an impressive 97% patient retention rate without using a traditional EDC system all while delivering a compliant, scalable, and cost-efficient solution.

It has become clear that our industry has reachedthe limits of human-only clinical development. As clinical trials have become increasingly complex, the endeavors that people alone can perform are no longer sufficient to generate the momentum needed to address the growing burden of human disease. This has led to longer drug development timelines and significant delays for patients. One large are of lost time is “white space,” definied simply as unproductive time caused by manual, sequential processes and fragmented data systems. Thankfully, a solution lies in agentic AI and its abilities to perform series of tasks.

The successful integration of AI agents in enterprise operations requires a balanced, deliberate approach. Drawing from recent research in Strategic Integration (SI) and agentic AI adoption within large enterprises, the following best practices help maximize value, manage change effectively, and mitigate common pitfalls. Medable Agent Studio specifically streamlines this process by providing robust tools, no-code simplicity, and built-in compliance and security standards.

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Deployment of remote and virtual clinical trial methods and technologies, referred to collectively as decentralized clinical trials (DCTs), represents a profound shift in clinical trial practice. To our knowledge, a comprehensive assessment of the financial net benefits of DCTs has not been conducted

Research looking at mobile apps and how they may provide a meaningful access point for all stakeholders for symptom management.

Explore how artificial intelligence (AI) is fundamentally transforming every facet of clinical trials, from initial protocol design and patient recruitment to data management and regulatory approval. This comprehensive guide provides an authoritative, in-depth look at AI's role in accelerating drug development and improving patient outcomes, with special focus on emerging agentic AI technologies.

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Discover how Agentic AI is revolutionizing clinical trials by optimizing efficiency, accelerating drug development, and improving patient access to therapies.

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