Agentic AI

AI-powered remote patient monitoring (RPM) is transforming clinical trials by enabling continuous data collection, real-time insights, and decentralized participation. This ecosystem spans wearables, AI analytics, data platforms, and decentralized clinical trial (DCT) infrastructure.

Additionally, agentic AI is fundamentally reshaping remote patient monitoring (RPM) in clinical trials by shifting it from passive data collection to proactive, autonomous decision support. Instead of simply aggregating data from wearables and patient-reported outcomes, agentic systems can continuously analyze multi-source trial data, identify emerging risks, and take action, such as prioritizing at-risk patients or sites, triggering alerts, or recommending interventions, without waiting for human input. This significantly reduces delays in detecting safety signals or protocol deviations. Just as importantly, agentic AI introduces workflow automation at scale by handling routine monitoring tasks, coordinating communications, and maintaining audit-ready reasoning trails. The result is a more adaptive and responsive RPM model where clinical teams move from manual oversight to strategic supervision, enabling faster, safer, and more efficient trials.

Below is a structured overview of the leading vendors, tools, and providers enabling AI-driven RPM in clinical research.

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Discover how AI-enabled eCOA and agentic workflows reduce clinical trial startup time, translation cycles, and meeting overhead—cutting eCOA build timelines from 16–20 weeks to under 8 weeks.

"The scarcest resource in clinical trials is the time of the highly qualified people running the clinical trials. We need to free up their time to bring more meaningful innovation to patients."

- David Hyman, Chief Medical Officer, Eli Lilly

Since the year 2000, the pace of drug approvals has remained stubbornly slow, with the FDA approving roughly 50 new treatments per year. This pace is in spite of massive increases in R&D investment. It’s well known that clinical trials take 10-12 years on average to complete all four phases. But what if clinical trials didn’t take weeks to start, months to enroll, and years to complete?

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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).

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There's a particular kind of energy at the World Vaccine Congress that's hard to describe unless you've been in it. Hundreds of scientists, executives, policymakers, pharma leaders, CRO teams, and site networks, all in one convention centre, all sitting with the same fundamental tension. We know how to make vaccines that save lives. So why does getting them to patients still take so long?

I spent three days in Washington this week as part of the Medable team. Washington in late March meant that the cherry blossoms were just past their peak but still stunning, and catching them along the Tidal Basin between sessions was one of those small, unexpected gifts that a busy conference week doesn't always make room for.

For the conference, I came in with a clear intention. I wanted to reconnect with partners I respect, listen more than I talk, and have honest conversations about where this industry is and where it needs to go. What I didn't fully anticipate was how much the week would reinforce something I already believed but needed reminding of.

The urgency is real. And it's shared.

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eCOA has moved from a supporting tool to a foundational pillar of modern clinical trials, and Everest Group agrees. In its inaugural eCOA Products PEAK Matrix Assessment, Everest named Medable a Leader, citing strong market impact, accelerated timelines, and a platform built for real-world trial complexity. As the eCOA market surges toward nearly $1B in value, this recognition underscores how speed, patient experience, and AI-driven innovation are reshaping how trials are designed, launched, and scaled globally.

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Each January, the J.P. Morgan Healthcare Conference sets the tone for the life sciences industry, serving as the year’s most influential gathering of biotech, pharma, investors, and dealmakers. 

This year was no different. 

Thus, we checked in with our conference attendees, booth visitors, and more to see what they thought were this year’s trend-setting takeaways.

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The numbers are in, and they spell change for life sciences. 

That’s because nearly three-quarters (73%) of global pharmaceutical organizations are actively planning, piloting, or deploying agentic AI initiatives. 

This widespread means agentic AI is no longer a futuristic concept, but a present-day imperative for staying competitive and delivering life-changing medicines faster.

For those not yet in the know, agentic AI is a sophisticated form of AI designed not just to analyze data, but to act autonomously, plan, reason, and execute complex, multi-step tasks. This goes far beyond traditional automation. Instead, agentic AI is about creating intelligent systems that can drive innovation, accelerate drug development, and optimize operations like never before.

So, which companies are leading the charge, and how exactly are these intelligent agents reshaping the pharma landscape? Let's dive into the specifics.

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It’s been said that the only constant in the world is change. 

For decades, clinical trials have been a human only endeavor, with teams of clinicians, study teams, and patients working hand in hand to bring the latest molecules to market. Now, a new central actor has entered the clinical paradigm, agentic artificial intelligence. 

Only three years after OpenAI kicked off the artificial intelligence arms race, AI has gone from requiring users to prompt it, to pre-emptively identifying bottlenecks, safety risks, and more, thanks to agentic AI. 

Agentic AI is an autonomous, goal-oriented system that uses reasoning and external tools to independently plan, execute, and adapt multi-step actions with minimal human intervention to achieve complex objectives. 

Sponsors and CROs have begun using AI agents across their workforces to improve trials in ways that humans have traditionally struggled to accomplish. For instance, organizations have been creating AI agents to analyze prior trial protocols, benefiting from lessons learned across prior trials and real-world outcomes, enabling teams to anticipate risks and automating elements of submission drafting. Anomaly detection has helped teams better identify outliers in operational metrics or safety signals, prompting early interventions. Document intelligence accelerates medical writing by grounding generative outputs in verified data, which reduces cycle time without sacrificing accuracy. 

However, in each of these use cases, humans remain squarely “in the loop.” Or rather, the decision making isn’t left entirely to AI. Instead, the objective is to augment clinical, regulatory, and legal teams with tools that surface the right information at the right time.

This core concept, keeping a human in the loop, is essential to clinical decision making and operations as agentic AIs, while powerful, are not inherently suited to fully autonomous operation in all regulated contexts.

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The 2025 ESMO AI & Digital Oncology Congress, held in Berlin from November 12 to 14, highlighted the accelerating role of artificial intelligence across the oncology care continuum. Although imaging and pathology remain the most established fields for AI adoption, this year’s programming revealed a decisive shift toward workflow-integrated AI that enhances clinical operations, supports trial efficiency, and addresses the realities of patient monitoring. 

Across three days of sessions, side-room conversations, and industry demonstrations, one theme was clear. AI is evolving from experimental add-on technology into a practical clinical teammate, but scaling its impact will require robust validation, seamless integration, and a sustained focus on clinician trust.

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Discover how Medable’s AI-powered oncology platform simplifies complex cancer trials by integrating eCOA, ePRO, and eConsent solutions—reducing trial time, improving patient retention, and enhancing data quality for faster, more efficient research.

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