AI-Assisted Clinical Evidence Synthesis Workspace
Speeds literature search screening and extraction for medical affairs and regulatory evidence packages Evidence basis: npj Digital Medicine reported human-AI workflows improved screening recall and reduced evidence synthesis time; benefits were strongest with expert oversight and remaining limits in extraction generalization
The Problem
“AI-Assisted Clinical Evidence Synthesis Workspace”
Organizations face these key challenges:
Speeds literature search screening and extraction for medical affairs and regulatory evidence packages
Impact When Solved
The Shift
Human Does
- •Define evidence questions and inclusion criteria
- •Manually screen literature search results for relevance
- •Extract study details and outcomes into shared trackers
- •Review evidence summaries and resolve inconsistencies
Automation
- •No AI-assisted screening or extraction support
- •No automated prioritization of relevant studies
- •No system-generated evidence summaries or flags
Human Does
- •Set review scope, evidence standards, and decision criteria
- •Validate AI-prioritized studies and confirm inclusion decisions
- •Review extracted evidence fields and correct exceptions
AI Handles
- •Prioritize literature search results for screening review
- •Flag potentially relevant studies based on evidence criteria
- •Draft structured extraction of study characteristics and outcomes
- •Generate evidence summary views for expert review
Operating Intelligence
How AI-Assisted Clinical Evidence Synthesis Workspace 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 make final study inclusion or exclusion decisions without reviewer judgment [S1].
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 AI-Assisted Clinical Evidence Synthesis Workspace implementations: