CIOMS XIV and the Governance Principles for Safe AI in Pharmacovigilance

CIOMS XIV and the Governance Principles for Safe AI in Pharmacovigilance

(Note: This episode was recorded on June 22nd 2026) Jason Bryant of ArisGlobal interviews pharmacovigilance consultant Denny Lorenz of Lorenz Bratti GmbH about the CIOMS XIV expert working group guidance on safely operating AI in pharmacovigilance. Lorenz explains the group began before ChatGPT and had to rework the guidance after November 2022, shifting from proprietary-model practices to enduring, principle-based guidance developed with regulators, academia, and industry. The principles are linked and anchored in a risk-based approach; "human oversight" is often misunderstood as sufficient on its own, but must be complemented by privacy, transparency, documentation, and governance. They discuss why AI requires ongoing monitoring and regression testing, including monitoring human–machine interaction and overtrust risk, and why many pilots stall due to unclear guardrails and metrics. An open question is how regulators will respond to reduced 100% human review once performance data is robust. 00:00 Meet the Hosts 00:55 CIOMS XIV Sets the Stage 01:49 Before and After ChatGPT 03:11 Stakeholders at the Table 03:58 Principles Over Technology 04:47 Risk Based Foundation 05:22 Human Oversight Explained 08:29 From Hype to Governance 09:36 Guardrails and Metrics 10:48 Enterprise Tools and Ad Hoc Use 11:32 Making Guidance Implementable 12:37 Why Monitoring Matters 13:19 Regression Testing Prompts 14:06 When AI Gets Too Good 15:07 Inspecting AI Reasoning 16:39 Pilot to Production Checklist 18:40 Dynamic Risk Governance 19:15 Oversight Without 100% Review 21:08 SMEs From Day One 23:10 Working Group Next Steps 24:07 Guidance Enables Trust

Trusting AI in Pharmacovigilance: From Proving It Works to Scaling Responsibly

In this AI Exchange podcast episode, Jason Bryant (ArisGlobal) and Sam Wallace (BMS) discuss how the conversation in life sciences has shifted from whether AI works to whether organizations are ready to trust it as it moves beyond workflow automation into judgment-heavy pharmacovigilance work. They describe progress at BMS in adverse event processing, including strong and improving F1 accuracy scores, expanded data extraction coverage, and the challenge of reducing 100% QC as trust, governance, and change management lag behind rapidly advancing technology. They explore perception gaps about AI maturity, the limits of pursuing "perfection" in probabilistic systems, and the promise of agentic AI, evaluators, and human-in/on-the-loop models to enable risk-based, regulator-friendly oversight, aiming toward touchless, explainable processing while prioritizing people, process simplification, and durable customer value over model novelty. 00:00 Welcome and Introductions 00:18 From Works to Trust 01:37 Agentic AI vs Humans 02:46 18 Months of Change 04:52 Perception vs Reality 07:13 Value Over Perfection 10:34 Why BMS Started 11:58 Scaling and Agentic QC 15:18 Trust in Practice 17:09 Designing for Trust 19:18 Agentic Work Delegation 19:56 Benchmarking Long Tasks 21:09 Redesigning Complex Workflows 22:52 Separating Hype From Reality 23:57 Vision Execution Governance 25:32 Touchless Safety Processing 27:08 Ask Me Anything Reflections 28:46 Fear Doubt And Trajectory 30:49 Predictability And Hybrid AI 11:17 People Over Technology 34:58 Closing Takeaways Trust

Regulatory Affairs in a Data-Driven World: Structured Data, IDMP, and the Evolving RA Role

The episode of the Life Sciences, AI Exchange features Hans Van Leeuwen (a regulatory consultant at ArisGlobal) and Ian Crone (Head of Regulatory Strategy and Growth at ArisGlobal) discussing how regulatory affairs (RA) is changing as pharma moves from document-based submissions to structured data. They note that companies still lack a true "single source of truth," relying on fragmented systems, manual handoffs, Excel trackers, and costly reconciliation that can create duplication, inconsistencies, and errors—pressures intensified by faster development and accelerated reviews post-COVID. They argue structured regulatory data (including IDMP) improves standardization, data quality, and internal communication, while enabling earlier detection of discrepancies as agencies increasingly use AI/ML to analyze submissions. They propose RA evolve from a submission-focused function to a product-data steward leading master data governance, API-enabled integrations, and unified RIM approaches, with structured master data hubs helping speed time to market and reduce delays. 00:00 Podcast Overview 00:38 Meet the Hosts 01:10 Data Everywhere in Pharma 02:37 No Single Source of Truth 04:51 Shift to Structured Submissions 07:06 IDMP as a Data Standard 09:08 RA as Data Steward 10:51 Culture Shift and AI Fears 12:30 Future RA in Development 15:16 Master Data Strategy and RIM 17:15 Wrap Up and Thanks

IDMP in Motion: Powering Compliance, Data, and the AI-Ready Future

Moderator: Ian Crone, ArisGlobal Panellists: * Remco Munnik (Arcana Life Sciences) * Peter Brandstetter (Accenture) * Frits Stulp (Implement Consulting Group) In this episode, the focus is on IDMP and its role in transitioning from document-based to data-driven regulatory affairs. Key points include the status of IDMP adoption within the EU, the readiness and challenges of current systems, and the potential of structured data to empower AI applications. The conversation highlights how structured, high-quality product data can enhance regulatory processes, from signal detection to submission automation. Participants stress the importance of robust data foundations, referencing examples from other industries and emphasizing the transformative potential of data governance. The discussion concludes with actionable steps for companies to make meaningful progress, emphasizing the alignment of leadership, structure, tooling, and mindset in embracing data and AI for improved healthcare delivery. 00:42 Episode Overview: IDMP in Motion 01:04 Guest Introductions 01:35 Current State of EU IDMP Adoption 03:01 EMA's Role and Data Management 08:04 AI Applications in Regulatory Affairs 12:22 The Importance of Data Quality 13:07 Future of AI and IDMP in Regulatory Affairs 17:35 Challenges and Solutions for Implementation 23:39 Predictions for the Future 28:45 Actionable Steps for Companies 32:12 Conclusion and Final Thoughts

Embracing Gen AI in Pharma Vigilance: A Deep Dive into Implementation and Impact

In this episode of the Life Sciences Gen AI Exchange Podcast, host Lucinda Smith, Chief Safety Product Officer at ArisGlobal, talks with Claudia Lehmann Head of Global PSPV Operations at Boehringer Ingelheim. They discuss the journey and practical application of AI in pharmacovigilance, the benefits of AI and automation over the past five years, and the current use of Gen AI to further improve processes. The conversation covers the decision-making process behind adopting AI, overcoming technical hurdles, change management, and the impact on roles and processes within the organization. Claudia highlights the importance of starting small, understanding and assessing risks, continuous learning, and evolving with technology in a highly regulated industry. 00:00 Introduction to the Life Sciences Gen AI Exchange Podcast 00:42 Guest Introduction and Episode Focus 01:02 Journey into AI in Pharmacovigilance 03:06 Challenges and Successes in AI Implementation 05:56 Decision-Making and Project Structure 15:18 Technical Hurdles and Model Validation 18:37 Change Management and Building Trust in AI 29:31 Lessons Learned and Recommendations 33:11 Conclusion and Future Outlook