AI Screening: Setting the Standard

Title/abstract screening is where most time is spent in systematic reviews. EvidAI's approach to AI-assisted screening represents a fundamental advancement over existing solutions.

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The Evolution of Screening Technology

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EvidAI's Multi-Agent Approach

How It Works

Instead of relying on a single AI model—which can hallucinate or have blind spots—EvidAI uses four different AI models that independently evaluate every paper:

Why Multiple Models Matter

THE CONSENSUS ADVANTAGE:

Single Model:
├── Has unique blind spots
├── Can hallucinate confidently
├── No self-correction mechanism
└── 89% sensitivity typical

Multi-Agent Consensus:
├── Models catch each other's errors
├── Disagreement flags uncertainty
├── Hallucinations detected via voting
└── 96%+ sensitivity achieved

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Performance Comparison

Accuracy Metrics

What These Numbers Mean

96.2% Sensitivity: For every 100 relevant studies in your database, EvidAI correctly identifies 96+. Missing only 4% compares favorably to human dual-review teams.

85% Auto-Decidable: For most reviews, 85% of papers are automatically processed without human intervention. This transforms a 4-week task into a 3-day task.

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Confidence Calibration

EvidAI doesn't just provide decisions—it provides calibrated confidence scores:

Why Calibration Matters

When the system says "90% confident," it means the decision is correct 90% of the time. This allows you to:

• Trust high-confidence decisions (>90%): Auto-process
• Focus human attention (70-90%): Review strategically
• Catch edge cases (<70%): Always manual review

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Screening Workflow Comparison

Traditional AI Screening

Paper Input
    │
    ▼
┌─────────────┐
│ Single Model │
│ (Black Box)  │
└──────┬──────┘
       │
       ▼
   Decision
   (Include/Exclude)
   
Problems:
• No reasoning visible
• No confidence measure
• Hallucinations undetected
• Cannot audit AI logic

EvidAI Multi-Agent Screening

Paper Input
    │
    ├────────────┬────────────┬────────────┐
    ▼            ▼            ▼            ▼
┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
│ GPT-4o │ │ Claude │ │ Gemini │ │ Custom │
│  35%   │ │  35%   │ │  20%   │ │  10%   │
└───┬────┘ └───┬────┘ └───┬────┘ └───┬────┘
    │          │          │          │
    └──────────┴─────┬────┴──────────┘
                     ▼
           ┌─────────────────┐
           │ Consensus Engine │
           │ • Weighted voting │
           │ • Confidence calc │
           │ • Disagreement    │
           │   detection       │
           └────────┬─────────┘
                    │
    ┌───────────────┼───────────────┐
    ▼               ▼               ▼
┌────────┐   ┌───────────┐   ┌────────┐
│ INCLUDE│   │  HUMAN    │   │EXCLUDE │
│ (>90%) │   │  REVIEW   │   │ (>90%) │
│  Auto  │   │(50-90%)   │   │  Auto  │
└────────┘   └───────────┘   └────────┘

Benefits:
• Full reasoning for every decision
• Calibrated confidence scores
• Disagreements flagged automatically
• Complete audit trail

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Audit Trail Example

Every screening decision includes complete documentation:

SCREENING DECISION RECORD
═══════════════════════════════════════════════════════════

Study: "Randomized trial of empagliflozin in type 2 diabetes"
Decision: INCLUDE
Confidence: 96.8%

MODEL VOTING:
┌──────────────┬──────────┬────────────┬──────────────────────┐
│ Model        │ Decision │ Confidence │ Key Reasoning        │
├──────────────┼──────────┼────────────┼──────────────────────┤
│ GPT-4o       │ Include  │ 97%        │ RCT, matches PICO    │
│ Claude 3.5   │ Include  │ 96%        │ Population eligible  │
│ Gemini 1.5   │ Include  │ 98%        │ Outcome relevant     │
│ EvidAI       │ Include  │ 96%        │ All criteria met     │
└──────────────┴──────────┴────────────┴──────────────────────┘

PICO EVALUATION:
├── Population (Adults with T2DM): MATCH ✓
├── Intervention (SGLT2 inhibitor): MATCH ✓
├── Comparator (Placebo): MATCH ✓
└── Outcome (Glycemic control): MATCH ✓

Routing: AUTOMATIC INCLUDE (confidence >90%, unanimous)
═══════════════════════════════════════════════════════════

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The Validation Behind These Claims

How We Validated

Ongoing Quality Assurance

• Every user override improves the system
• Continuous recalibration from feedback
• Domain-specific accuracy tracking
• Regular validation against gold standards

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What This Means for Your Research

Time Impact

Quality Impact

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Summary

EvidAI's multi-agent screening isn't just incrementally better—it's a fundamentally different approach that achieves accuracy levels single-model systems cannot match.

The combination of multiple AI models working in consensus, calibrated confidence scores, complete audit trails, and continuous validation creates a screening system you can trust for even the most demanding regulatory submissions.