Agentic AI


What to look for in an AI clinical trial platform: A buyer's guide
Discover what to look for from artificial intelligence tools in the clinical trial market.


Which AI and agentic AI clinical trial vendors integrate best with existing systems?
Artificial intelligence is reshaping clinical trial operations in 2026. Sponsors and CROs are no longer evaluating standalone tools. They are choosing platforms that can connect with existing systems, automate complex workflows, and scale without disrupting ongoing studies. This guide breaks down the vendors best positioned to integrate with the clinical trial infrastructure you already have.


Key criteria for evaluating AI and agentic AI clinical trial vendors
Artificial Intelligence is rapidly transforming clinical research. From patient recruitment and protocol design to medical writing and data review, AI-powered solutions are becoming embedded across the clinical development lifecycle. More recently, the emergence of Agentic AI (systems capable of planning, reasoning, and executing multi-step workflows with varying degrees of autonomy) has generated significant excitement throughout the industry.
However, not all AI solutions are created equal. While many vendors promise dramatic improvements in efficiency and productivity, clinical trial organizations operate in one of the most highly regulated environments in the world. Success depends not only on technical performance but also on compliance, validation, governance, security, and trust.
As sponsors, CROs, and technology teams evaluate potential AI partners, they need a framework that extends beyond traditional software procurement criteria. The following considerations can help organizations assess both AI and Agentic AI vendors and identify solutions that are truly ready for clinical research.


Ontology 101: The semantic layer behind modern life sciences data
Clinical data speaks dozens of languages. Ontologies are the translator. Discover how life sciences teams are using semantic layers, AI agents, and MCP connectors to cut months of data harmonization down to days.


The AI Pilot Trap and How Clinical Trial Leaders Can Escape It
Most AI pilots in clinical trials fail to scale beyond proof of concept. Learn practical strategies for moving from isolated experiments to enterprise adoption.


The best AI tools for remote patient monitoring in clinical trials
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.


From three meetings to one removing bottlenecks with AI-enabled eCOA
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 1:1:1 vision: Reimagining clinical development
"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?

eCOA, AI, and Agentic AI: A practical overview and guide
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).


From the Congress floor to the dinner table: A Week in Washington that reminded me why this work matters
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.


Harnessing AI for more efficient clinical trials
Explore how AI is transforming clinical trials, from accelerating data analysis to predicting trial outcomes.


Everest analysis: How Medable eCOA solves speed, patient experience, and customer needs
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.


What happened at JPM 2026?
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.


