ENCePP has asked its Artificial Intelligence Working Group to conduct this survey to understand how its members and the broader PE/PV community use AI tools in pharmacoepidemiology and pharmacovigilance.
Every question in this survey was shaped by comments and suggestions from ENCePP WG members. This initiative directly incorporates input from the February 2026 WG meeting, the ARCS PV survey, and the infrastructure subgroup contributions led by WG colleagues. Your prior engagement is woven into every section below.
This survey is conducted in compliance with GDPR (EU 2016/679). The University of Copenhagen is the data controller. No personally identifying information is collected — responses are entirely anonymous and cannot be linked to individual participants. Data will be stored securely on SurveyXact (EU data protection standards, EEA storage) for five years and deleted thereafter. Results may be published in aggregate form in academic publications, ENCePP newsletters, and related venues. By continuing, you confirm you have read this information and consent to participate.
Your answer determines which tailored module you will receive.
A few background questions to help us understand respondents. All data is reported in aggregate only.
Two sub-questions below capture your professional profile. Responses are reported in aggregate only — in the unlikely event that only a small number of respondents select a category, results for that category will not be reported separately.
Options will reflect your selected track.
These questions apply to all respondents and build the ENCePP community inventory of AI adoption across PE and PV settings.
These questions were developed by the ENCePP Infrastructure Subgroup (JD, FR, MG) and Barriers Subgroup (AL, CP, DL), specifically requested during the February 2026 WG meeting.
This question maps available data infrastructure — select all sources your organisation has access to for AI-driven work, even if not yet fully used.
1 = No understanding · 5 = Full expert understanding
| Term | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Machine learning (ML) | |||||
| Natural language processing (NLP) | |||||
| Large language models (LLMs) | |||||
| Predictive analytics | |||||
| Signal detection algorithms |
Now that you have worked through all the specific questions above, please give us your overall assessment:
This final section is shared by all respondents and captures your personal views, community priorities, and open feedback for the ENCePP WG.
SD = Strongly Disagree · D = Disagree · N = Neutral · A = Agree · SA = Strongly Agree
| Statement | SD | D | N | A | SA |
|---|---|---|---|---|---|
| Generative AI has already changed how PE/PV is conducted | |||||
| Ethical concerns about AI in PE/PV need more attention | |||||
| AI threatens the job security of PE/PV professionals | |||||
| Regulatory guidance on AI in PE/PV is sufficiently clear | |||||
| My organisation is ready to scale AI applications in PE/PV | |||||
| AI tools currently used in my field are trustworthy and validated |
This question asks for your personal or organisational priority — distinct from the objective infrastructure assessment in Section 3.
Free text — share as much or as little as you like
Please mention specific topics, formats, or time commitments you would find valuable
Your suggestions directly feed into the WG's work programme and survey revisions
Document every AI tool you have used in PE or PV — one structured entry per tool. This builds the ENCePP inventory of AI applications across the community.
Your input helps us understand how AI is — and should be — used in pharmacoepidemiology and pharmacovigilance across Europe. Your voice is heard, and your response will directly inform the ENCePP WG's evidence base and future work programme.