Adaptive Trial Design Intelligence
Adaptive Trial Design Intelligence uses advanced AI to design, simulate, and optimize clinical trial protocols in real time across decentralized, adaptive, and externally controlled designs. It integrates real‑world data, trial evidence, and discovery insights to refine eligibility criteria, dosing strategies, and sample sizes as new data emerge. Sponsors gain faster time to statistical readouts, higher trial success probabilities, and more capital‑efficient drug development programs.
The Problem
“Adaptive Trial Design Intelligence for faster, lower-risk clinical development”
Organizations face these key challenges:
Protocol design knowledge is buried in unstructured documents and siloed expert workflows
Decentralized trial activities are difficult to map correctly across remote, hybrid, and site-based settings
External control arm construction is methodologically complex and labor-intensive
Eligibility criteria are hard to analyze systematically for recruitment and outcome impact
Recruitment and trial data are fragmented across EDC, CTMS, ePRO, labs, and site systems
Static trial simulations become outdated as new evidence emerges
Regulatory expectations for adaptive and real-world evidence designs are difficult to operationalize consistently
Manufacturing quality and clinical development signals are not integrated for end-to-end decision-making
Offline treatment policy optimization is difficult when data is sparse and online experimentation is unsafe
Impact When Solved
The Shift
Human Does
- •Review prior trial evidence, real-world data, and protocol assumptions manually
- •Define eligibility criteria, endpoints, dosing plans, and sample size through expert discussion
- •Monitor recruitment, safety, and data quality through periodic cross-functional review
- •Decide protocol amendments and go/no-go actions based on incomplete interim information
Automation
- •No material AI support in the legacy workflow
- •Limited use of static analytics for reporting and summaries
- •Minimal automated signal detection across trial and external data
Human Does
- •Approve adaptive design choices, protocol changes, and statistical decision rules
- •Review AI-generated recommendations for eligibility, dosing, sample size, and control strategy
- •Handle safety, compliance, and data-integrity exceptions requiring clinical judgment
AI Handles
- •Continuously analyze trial data, historical evidence, and real-world data to refine design assumptions
- •Simulate adaptive, decentralized, and externally controlled trial scenarios and compare tradeoffs
- •Monitor recruitment, retention, safety, and data quality signals and triage emerging risks
- •Generate protocol optimization recommendations for eligibility criteria, dosing strategy, and sample size
Operating Intelligence
How Adaptive Trial Design Intelligence runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not change protocol design, statistical decision rules, or study continuation status without approval from designated clinical, biostatistics, and regulatory leaders.[S10][S12]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Adaptive Trial Design Intelligence implementations:
Key Players
Companies actively working on Adaptive Trial Design Intelligence solutions:
Real-World Use Cases
Agentic AI for clinical trial patient recruitment and data analysis
AI agents read trial rules, search patient records safely, find eligible participants, and combine incoming trial data so teams can make decisions faster.
Real-time agentic clinical trial success prediction and monitoring
An AI team watches clinical trials like expert analysts, predicts whether a study is likely to succeed, and keeps updating that prediction as new evidence appears.
Offline RL optimization of adaptive treatment strategies with treatment stitching
The system learns better treatment plans from old patient records by combining pieces of real treatment journeys and filling gaps between similar patient states, instead of experimenting on live patients.
Explainable protocol editing support for trial design review
After scoring a new trial protocol, the system points to the specific eligibility rules and design features that pushed the prediction toward failure or success, so clinicians can edit the protocol and try again.
AI protocol mapping for decentralized trial design and site execution
Use AI to turn FDA decentralized trial guidance into a checklist that maps each study activity to the right setting, such as telehealth, home visit, local provider, or traditional site.