I’ve decided to go much deeper into drug safety and pharmacovigilance.
Over the past year, we discovered that our LLM-based NLP approach works surprisingly well for PV. That led us to develop the IPV tool, and the results convinced me that AI can truly make a difference in this field. The challenge is that PV is not my original area of expertise. But just as I did when I learned deep learning or cancer genomics, I want to learn it properly and eventually become very good at it.
Naturally, I asked AI for guidance.
When I asked ChatGPT 5.1 how I should learn PV, the first answer told me I was “over-qualified for a PV scientist.” Amusing, but not what I needed. After I explained that my goal is to truly understand the field so we can build better tools that help patients and PV teams, it finally gave me a thoughtful 6-month study plan. I then asked Claude to improve it and make it more structured.
Together, they produced a roadmap that is much more serious than I expected.
The plan walks through the full PV landscape:
• PV foundations, regulations, and global systems (FDA, EMA, PMDA)
• MedDRA, WHO Drug Dictionary, terminology, and case elements
• ICSR structure, triage rules, QC/QA steps, and medical narrative writing
• Signal detection using PRR, ROR, EBGM, IC, and time-to-onset analysis
• Literature monitoring, evidence grading, deduplication, and automation
• GVP compliance, validation, audit trails, and AI explainability
• A capstone project integrating FAERS, literature, and drug labels into a safety knowledge graph
It also includes weekly hands-on assignments: reviewing real FAERS cases, coding MedDRA terms manually, rewriting safety narratives, auditing LLM outputs, building disproportionality models, and creating structured evidence summaries. This is the kind of deep, practical learning I enjoy.
Why am I doing this?
Because if we want to push AI-powered PV forward, I need to truly understand the people, workflows, constraints, and regulatory expectations behind the field. Technology alone isn’t enough. Good AI tools come from understanding the domain as deeply as the practitioners who work in it every day.
I plan to learn actively from my LinkedIn network along the way. I’ll ask questions, share what I’m studying, and post insights as I go. Six months from now, I hope to return to this post and reflect on how much I’ve learned.
For those working in PV or drug safety: What resources, habits, or practical tips would you recommend for someone who wants to deeply learn this field?
#pharmacovigilance #drugsafety #artificialintelligence #machinelearning #LLM #AIinHealthcare #PVtech #MedDRA #FAERS #literaturemonitoring