When Algorithms Stop at Data, Human Judgment Protects Patients
Welcome back, Pharma & Life Sciences colleagues,
In the previous edition, I spoke about AI as a force
multiplier in pharmacovigilance not a replacement for human intelligence. This
time, I want to go a level deeper and ground that discussion in real situations
many of us have either witnessed or lived through.
Human review in PV is not a procedural checkbox. It is the
ethical and clinical foundation of drug safety.
1. When AI Sees Patterns, Humans See Context
AI is excellent at volume. It can process thousands of
adverse event reports overnight. But volume without context can mislead.
The “Headache” That Wasn’t An AI system
flagged a cluster of “severe headache” reports linked to a newly launched
migraine drug. Automated causality scoring marked it as a probable adverse
reaction.
2. Statistical Correlation Is Not Clinical Judgment
Another example, this time from a mid-sized pharmaceutical
organization:
The Elderly Patient “Fall” Signal an AI
system monitoring EHR data flagged a rise in falls and fractures among elderly
patients taking a widely prescribed antihypertensive. Statistically, the signal
was strong.
3. Novel Risks Demand Human Interpretation
The COVID-19 vaccine rollout put global PV systems under
unprecedented pressure. AI helped manage scale but not uncertainty.
The Rare Blood Clot Cases Early reports of
CVST following vaccination did not match any historical patterns. AI models
trained on pre-pandemic data had no reference point.
4. Language and Culture Still Matter
Global pharmacovigilance is not just multilingual it is
deeply cultural.
“My Heart Feels Heavy” Social media
monitoring tools flagged posts describing a “heavy heart” in patients taking an
antidepressant and categorized them as potential cardiac events.
5. Accountability Cannot Be Automated
Perhaps the most important question is not what AI can do
but who remains accountable.
The Missed Anaphylaxis Case An AI-based
prioritization model deprioritized a fatal allergic reaction because the
reporter used lay language (“throat closed up”) instead of clinical terminology
(“laryngeal edema”).
Building PV Systems That Are Actually Safer
These are not edge cases. They are everyday realities across
pharmacovigilance teams worldwide.
- AI
is a powerful assistant it can triage, cluster, and prioritize.
- Humans
remain the decision-makers they assess context, apply judgment, and take
responsibility.
Practical, experience-based principles:
- Every
AI-generated signal must be validated by a qualified reviewer.
- Train
models on diverse data but never assume they understand human nuance.
- Require
documented justification when experts override AI outputs.
- Encourage
regular cross-functional reviews involving clinicians, data scientists,
and PV professionals.
Let’s Discuss
Have you seen situations where human expertise corrected an
AI-driven conclusion? Or where AI meaningfully reduced noise and allowed teams
to focus on higher-value analysis?
Share your experiences practical insight from the field is
how better PV systems are built.
Remember: In pharmacovigilance, we are not
processing reports we are protecting people. Technology should support human
judgment, not replace responsibility.
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In the age of artificial intelligence, human wisdom must
remain at the center of drug safety.