Pharma Evidence Intelligence Suite
Pharma Evidence Intelligence Suite uses advanced AI to discover, analyze, and synthesize clinical, real‑world, and regulatory evidence across the global literature and key data sources. It automatically surfaces relevant studies, extracts critical endpoints and safety signals, and generates traceable, regulator-ready insights to support drug development and medical affairs. This accelerates evidence generation, reduces manual review effort, and improves decision quality across the pharmaceutical R&D lifecycle.
The Problem
“Pharma and biotech teams struggle to keep up with the exploding volume of clinical and preclinical literature needed for evidence generation (e.g., for trial d…”
The Shift
Human Does
- •Define evidence questions and review scope for development, safety, or medical affairs needs
- •Search literature and data sources manually, then collect and organize relevant studies
- •Read full texts and abstracts to extract endpoints, comparators, outcomes, and safety findings
- •Discuss findings across stakeholders and decide what evidence supports next actions
Automation
Human Does
- •Set evidence objectives, inclusion criteria, and acceptable use boundaries for each review
- •Validate high-impact findings, adjudicate ambiguous evidence, and handle exceptions
- •Approve evidence summaries, safety interpretations, and regulator-facing conclusions
AI Handles
- •Continuously scan approved literature and data sources to identify relevant new evidence
- •Rank, cluster, and triage studies by indication, population, endpoint, and relevance
- •Extract key study details, endpoints, safety signals, and real-world outcomes into structured summaries
- •Generate traceable evidence syntheses and draft regulator-ready insight packages with citations
Operating Intelligence
How Pharma Evidence Intelligence Suite runs once it is live
Humans set constraints. AI generates options.
Humans choose what moves forward.
Selections improve future generation quality.
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
Define Constraints
Step 2
Generate
Step 3
Evaluate
Step 4
Select & Refine
Step 5
Deliver
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
Humans define the constraints. AI generates and evaluates options. Humans select what ships. Outcomes train the next generation cycle.
The Loop
6 steps
Define Constraints
Humans set goals, rules, and evaluation criteria.
Generate
Produce multiple candidate outputs or plans.
Evaluate
Score options against the stated criteria.
Select & Refine
Humans choose, edit, and approve the best option.
Authority gates · 1
The system must not finalize regulator-facing conclusions or safety interpretations without review and approval from the accountable medical, clinical, or safety lead. [S2]
Why this step is human
Final selection involves taste, strategic alignment, and accountability for what actually moves forward.
Deliver
Prepare the selected option for operational use.
Feedback
Selections and outcomes improve future generation.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Pharma Evidence Intelligence Suite implementations:
Key Players
Companies actively working on Pharma Evidence Intelligence Suite solutions:
Real-World Use Cases
NIR-based prediction of API and HPMC content for downstream dissolution modeling
AI reads near-infrared spectra to estimate how much drug and polymer are in a tablet blend, then uses those estimates to help predict final tablet performance.
Automated rule-level assessment of trial exclusion criteria impact
Instead of treating all trial rules as equally important, AI checks each rule one by one to see which ones exclude lots of people without changing outcomes much.
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.
Centralized statistical monitoring to flag atypical trial sites early
An AI/statistical system watches data coming from many clinical trial sites and warns when one site looks unusually different from the others, so teams can check for data quality problems sooner.
AI-assisted generation of disclosure-ready RWE submission packages for FDA meetings
Use AI to turn a sponsor’s study idea into a clean FDA meeting package that includes the right study details while separating what can and cannot be publicly disclosed.