Designing Trustworthy AI for Regulated Life Sciences Decisions
Creating a decision accountability layer for AI-assisted pharmacovigilance that captures how AI suggestions become regulated human decisions
Pharmacovigilance focus · Framework reusable across Clinical & RWE
Key Insight & Differentiation
AI failure in regulated life sciences is not primarily a model accuracy problem. The deeper, largely undiscovered problem is:
There is no Decision Accountability UX layer that captures how AI suggestions turn into regulated human decisions.
Problem Discovery
The Visible Problem: Low Trust in AI Outputs
Pharmacovigilance teams at global pharmaceutical companies process thousands of Individual Case Safety Reports (ICSRs) monthly. AI systems classify seriousness, prioritize cases, and generate draft narratives—but medical reviewers don't trust these outputs.
No Explainability
AI classifications lack clinical justification
Hidden Uncertainty
Confidence levels and limitations unclear
No Evidence Trail
Cannot trace AI output to source data
The Less-Obvious Problem
The Hidden Gap: No Decision Accountability Layer
AI failure in life sciences is not primarily a model accuracy problem. The deeper, largely undiscovered problem is:
There is no Decision Accountability UX layer that captures how AI suggestions turn into regulated human decisions.
What Current Systems Show
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AI outputs and predictions
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Classification labels
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Confidence scores
What They Don't Capture
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Who made the final decision
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Why the decision was made
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How AI influenced (or didn't) that decision
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What evidence justified it
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How can it be defended months or years later
This Gap Creates
Trust Breakdown
Low AI Adoption
Silent Compliance Risk
Inspection Vulnerability
Research Foundation
Evidence-backed approach to AI trust and accountability
This design is grounded in peer-reviewed research, regulatory guidance, and industry best practices for trustworthy AI in regulated healthcare environments.
Explainable AI & Trust in Healthcare
Research shows lack of explainability and transparency is a major barrier to clinician trust and AI adoption in healthcare settings.
AI in Pharmacovigilance
Studies highlight that AI can automate PV tasks but stress the critical need for human oversight, traceability, and accountability.
Trustworthy & Responsible AI
Governance research emphasizes accountability, transparency, and maintaining human responsibility in AI-assisted decision-making.
Synthesis of Research Findings
What Research Confirms
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Explainability is critical for clinical trust
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Human oversight is a regulatory expectation
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Accountability must be traceable and auditable
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Uncertainty communication reduces risk
What Research Reveals Is Missing
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→ Few systems capture decision accountability
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→ AI-human handoff is poorly designed
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→ Post-decision auditability is an afterthought
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→ No standard UX pattern for regulated AI decisions
Research Foundation
Problem → Research → Insight → Design
This flow demonstrates how research insights directly informed design decisions, showing a rigorous, evidence-based approach to solving complex AI trust challenges.
Problem
Trust & accountability gaps
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Medical reviewers don't trust AI outputs
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No capture of decision accountability
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Cannot defend decisions in inspections
Research
Multi-disciplinary investigation
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XAI in healthcare literature
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AI in pharmacovigilance studies
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Regulatory & ethical AI principles
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PV workflow analysis
Insights
Decision accountability is missing
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Trust breaks at AI → human handoff
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Reviewers need evidence, not scores
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Uncertainty must be explicit
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Responsibility for decisions not captured
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Audit readiness bolted on, not designed in
Principles
Designing for accountability
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Explainability by design
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Evidence-first interfaces
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Explicit uncertainty
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Human-in-the-loop by default
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Decision accountability as first-class UX
Solution
Trusted AI Safety System
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AI Explainability Panel
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Evidence Traceability
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Decision Accountability Layer
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Automated audit trails
Design Challenge
"How might we design an AI-assisted pharmacovigilance system that medical reviewers can trust, understand, and defend during regulatory inspections?"
he answer: Build a Decision Accountability Layer that captures not just AI outputs, but how those suggestions become human decisions with full evidence, justification, and audit readiness.
Design Principles
Five principles for trustworthy AI in regulated systems
These principles emerged directly from research insights and guided all design decisions. They prioritize trust, transparency, and regulatory compliance over automation efficiency alone.
Explainability by Design
Every AI decision must be accompanied by clinical justification in language medical reviewers understand—not as an afterthought, but as a core system requirement.
Application in Design
Built explainability panels directly into case review workflows with clinical evidence highlighting
Evidence-First Interfaces
Show source evidence (narratives, labs, timelines, MedDRA codes) before predictions or confidence scores. Reviewers think in clinical signals, not statistical outputs.
Application in Design
Designed evidence traceability that links every AI output to specific source clinical data points
Explicit Uncertainty Communication
Confidence levels and areas of uncertainty must be communicated clearly and honestly. Hidden uncertainty increases regulatory risk and erodes trust.
Application in Design
Created confidence indicators, uncertainty factors display, and low-confidence warnings for reviewers
Human-in-the-Loop by Default
AI assists medical decisions; it never replaces human judgment. This must be structurally and visually enforced in every workflow.
Application in Design
Designed workflows that always require medical reviewer validation, justification, and explicit decision capture
Decision Accountability as First-Class UX
The system must capture WHO decided, WHY they decided, HOW AI influenced them, and WHAT evidence supported it—creating a complete audit trail by design.
Application in Design
Built Decision Accountability Layer with mandatory justification, before/after comparison, and timestamped audit logs
Solution Framework
Trusted AI Safety System with Decision Accountability Layer
Rather than improving AI models themselves, the most impactful opportunity lies in designing a comprehensive interaction layer that sits between AI outputs and medical reviewers.
This Layer Provides
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AI explanations in clinical terms
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Evidence and data lineage
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Confidence and uncertainty display
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Structured human override
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Automated audit-ready records
Framework Is Reusable
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Pharmacovigilance (drug safety)
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Clinical trial monitoring
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Real-world evidence (RWE) analysis
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Any regulated healthcare AI context
Design Solution
Trusted AI Safety System for Pharmacovigilance
Three core interfaces that create transparency, traceability, and decision accountability in AI-assisted safety decision-making.
Case Intake & AI Triage Dashboard
Dashboard showing incoming safety report with AI seriousness classification leveals, and low confidenace warnings
Key Features
✓AI classification with confidence scores
✓Visual confidence indicators
✓Low-confidence alerts
✓Primary CTA: "Explain AI Decision"
AI-Assisted Case Triage Dashboard
Incoming safety reports with AI classification and confidence indicators
Total Pending
38
AI: Serious
12
AI: Non-Serious
26
Low Confidence
5
ICSR-2024-00847
Healthcare Professional. 2h ago
Patient & Product
Patient ID: 48372 • Product XYZ 50mg
Reported Event
Acute liver injury
AI Classification
Serious
Explain AI Decision
Review Case
ICSR-2024-00848
Patient Report 3h ago
Patient & Product
Patient ID: 39201 • Product ABC 100mg
Reported Event
Headache, nausea
AI Classification
Non-Serious
Explain AI Decision
Review Case
ICSR-2024-00849
Literature 5h ago
Patient & Product
Patient ID: 55629 • Product XYZ 25mg
Reported Event
Possible anaphylaxis
AI Classification
Explain AI Decision
Review Case
Serious
Low Confidence Classification
AI confidence is below 70%. Manual medical review recommended. Click "Explain AI Decision" to see evidence and uncertainty factors.
Ai Explainablilty & Evidence Panel
Deep-dive panel showing clinical driver, source evidence tracebility, uncertainty factors, and similar historical case
Key Features
✓Clinical drivers ranked by impact
✓Source evidence with direct quotes
✓Uncertainty factors explicitly stated
✓Similar past cases for context
CASE ID: ICSR-2024-00847
AI Explainability & Evidence Panel
AI Classification
Serious
Confidence
89%
Why AI Classified This Case as Serious
The AI identified 3 clinical drivers indicating seriousness: explicit hospitalization mention, significantly elevated liver enzymes, and MedDRA coding for acute liver injury. These factors align with ICH E2A seriousness criteria.
Clinical Drivers (Ranked by Impact)
Hospitalization Mentioned
High Impact
AI Classification
Confidence
Case narrative, line 12
"Patient was admitted to hospital for observation"
94%
Liver Function Tests Elevated
High Impact
AI Classification
Confidence
Lab results, Day 3
ALT: 245 U/L (normal: 7-56), AST: 198 U/L (normal: 10-40)
94%
MedDRA Term: Acute Liver Injury
High Impact
AI Classification
Confidence
MedDRA coding
Coded as PT: 10019851
94%
Uncertainty Factors
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•Concomitant medication (paracetamol) may contribute to liver injury
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•Patient medical history incomplete - pre-existing condition unclear
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•Exact hospitalization duration not specified in narrative
Similar Past Cases
ICSR-2024-00621
Similarity: 87%
Confirmed Serious
ICSR-2024-00534
Similarity: 82%
Confirmed Serious
This explanation is automatically logged for audit purposes
Proceed to Medical Review
Decision Accountability Workspace
Captures the decision moment: how AI recommendations become human decisions with full justification and audit trail
Key Features
✓AI recommendation vs. human decision
✓Mandatory justification requirement
✓Before/after state preservation
✓Auto-generated audit trail
CASE ID: ICSR-2024-00847
Decision Accountability Workspace
Capturing how AI suggestions become regulated human decisions
Medical Reviewer
Dr. Sarah Chen, MD
AI Recommendation
Serious
Primary Criterion
Hospitalization
AI Confidence
89%
View Full AI Explanation
Your Medical Decision
Select your decision and provide clinical justification. This creates the accountability record.
Accept
Agree with AI classification
Modify
Accept with modifications
Reject
Override AI classification
Decision Accountability Record (Auto-Generated)
This record captures WHO, WHY, HOW, and WHAT for regulatory defense
WHO decided:Dr. Sarah Chen, MD (User ID: SC847)
WHEN decided:20/12/2025, 22:41:19
AI recommended:Serious (89% confidence)
Audit trail ID:AUD-2024-7890
Save Draft
Return to Queue
Submit Decision & Generate Audit Record
Design Fidelity & Context
These screens are mid-fidelity mockups designed to demonstrate core UX principles, interaction patterns, and the Decision Accountability Layer concept. In a full product implementation, they would:
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Integrate with existing enterprise PV systems (Oracle Argus, ArisGlobal, Veeva Vault)
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Connect to regulatory databases (MedDRA, WHO-DD, ICH E2B standards)
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Support multi-language, multi-region compliance requirements
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Include role-based access controls and user permissions
Reflection & Impact
Designing for trust, accountability, and regulatory defense
Research-Driven
Grounded in peer-reviewed XAI literature, PV research, and regulatory AI principles
Novel Framing
Identified Decision Accountability as an undiscovered gap in AI-assisted workflows
Systems Thinking
Addressed UX layer gaps rather than model performance optimization alone
Transferable
Framework reusable across Clinical Trials, RWE, and other regulated AI contexts
What I Learned
AI adoption in regulated healthcare depends as much on trust architecture as it does on model accuracy. Medical reviewers need more than explainability—they need a system that captures decision accountability: WHO decided, WHY, HOW AI influenced them, and WHAT evidence supports it.
This project reinforced that the most impactful design opportunities in AI are often not about improving the AI itself, but about designing the interaction layer where AI suggestions become human decisions. In regulated environments, that layer must be transparent, traceable, and defensible.
Next Steps
Validate with Medical Reviewers & Safety Physicians
Conduct usability testing with actual PV professionals to validate the Decision Accountability Layer design and refine the explainability panel interactions
Regulatory Alignment Review
Ensure design meets FDA, EMA, and PMDA expectations for AI transparency, human oversight, and audit trail requirements in pharmacovigilance systems
Expand Framework to Clinical & RWE
Apply the Decision Accountability Layer framework to clinical trial monitoring, real-world evidence analysis, and other regulated life sciences AI applications