Knowledge Center

What does it mean to make meaningful change? More than a decade ago, Medable was founded on a simple premise: bringing treatments to patients faster. What we encountered when we first started was an industry relatively moving at a fixed pace, approving only about 50 new treatments a year on average. This average remained constant since the year 2000, no matter how much R&D investment and new technology got poured in.

As the decade progressed and we developed our data platform, eCOA, eConsent, and Televisit tools, a vision began to take shape. This vision was a series of ambitious goals that we believe we can achieve within our lifetimes: our 1:1:1 vision. 

More importantly, we’ve made meaningful, measurable, tested progress towards radically redefining how fast clinical trials can move, and how they can be made easier for every stakeholder involved in them. This rapid evolution of Medable and the industry has been meaningful, with clients in top-10 pharma deploying our latest technologies, helping us take major steps towards accomplishing our 1:1:1 vision. 

‍

American biotechnology still leads in scientific innovation, but its advantage in turning science into medicine is eroding. Part 1 examines how slower, more expensive clinical development is shifting the competitive edge toward China, and what’s at stake for the future of U.S. biotech.

The ground has shifted for FSO (functional service outsourcing) and Unitized FSP (functional service providers) contract research organizations (CROs). 

For years, the playbook for managing these types of CRO economics was familiar. Sponsors negotiated rates while CROs managed headcount and utilization around those rates .

That playbook still holds true today in theory. However, the conditions underneath it have shifted enough that it no longer produces the results it used to. Funding is tighter, sponsors are smaller and more price-sensitive, timelines are compressing ahead of the patent cliff, and AI has moved from an experiment on the roadmap to a baseline expectation in every RFP.

None of that is unique to any one segment of the market, but these pressures land differently for FSO and unitized FSP providers than they do for full-service CROs. That’s because these businesses are built on rate cards for CRAs, monitors, and other functional resources. When sponsors squeeze rates or expect more output per unit, there's no broader program fee to absorb the hit, here the unit economics are the business.

‍

Clinical data doesn't need to be perfect for AI to deliver value. Learn a practical framework for deploying agentic AI in clinical trials today.

‍

According to seven different market research organizations, the “Agentic AI in clinical trials market” is expected to grow at a compound annual growth rate (CAGR) of anywhere between 12.5% to 43%. While the estimates obviously vary depending on who you ask, one thing remains crystal clear, agentic AI in clinical trials isn’t slowing down. As sponsors and CROs increasingly implement artificial intelligence, many organizations approaching clinical AI face real anxiety about the clinical trustworthiness of AI.

With GLP-1 therapies reshaping the competitive landscape, sponsors need speed to first patient, portfolio consistency, and adherence that holds up over years, not weeks. See how five sponsors/CROs got there with Medable.

Industry leaders share real-world insights on using agentic AI to streamline clinical trials, accelerate decisions, and improve study execution.

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.

Informed consent is the first data point in every clinical trial, and one of the easiest to get wrong. Discover how multimedia consent, embedded comprehension checks, and smarter re-consent workflows help sponsors turn eConsent into a real data quality lever, not just a digital signature.

‍

The clinical trials industry is at an operational inflection point. Despite decades of process refinement, drug development timelines have continued to lengthen, and manual workflows still account for a significant portion of skilled operator time across key trial functions. The emergence of large language model-based AI platforms has introduced a new variable into this equation, with multiple vendors now positioning their technology as a solution to these structural inefficiencies.

Two categories of vendors appear frequently in sponsor and CRO evaluations. The first are  general-purpose AI platforms, whose large language model capabilities have become some of the most widely benchmarked in the industry. The second are purpose-built clinical technology platforms with an established operational presence across decentralized and hybrid trial environments. 

Both have made substantive investments in life sciences AI in 2026. Their approaches, however, differ significantly in architecture, regulatory readiness, and deployment scope. This analysis examines those differences to help sponsors and CROs assess which type of platform is better suited to their operational requirements.

‍

Combining artificial intelligence (AI) and agentic AI with electronic Clinical Outcome Assessment (eCOA) systems fundamentally enhances how clinical trial data is collected, interpreted, and acted upon. At its core, eCOA captures structured data directly from patients, clinicians, or observers, such as symptom severity, quality of life, or functional outcomes. Modern platforms expand this further by supporting a full range of assessment types, including electronic patient-reported outcomes (ePRO), clinician-reported outcomes (eClinRO), observer-reported outcomes (eObsRO), and performance outcomes (ePerfO).

‍