--- title: "How AI Is Reshaping Medical Information: What Pharma Companies Need to Prepare For" description: "Discover how AI is transforming Medical Information, why regulators are increasing oversight, and how pharma companies can prepare for a future-ready MI operating model." url: https://qbdgroup.com/en/blog/ai-medical-information-pharma type: "Blog post" language: en published: 2026-07-22 author: "María Corrales" category: "Pharmacovigilance" publisher: "QbD Group" citation: "QbD Group, \"How AI Is Reshaping Medical Information: What Pharma Companies Need to Prepare For\", https://qbdgroup.com/en/blog/ai-medical-information-pharma" --- # How AI Is Reshaping Medical Information: What Pharma Companies Need to Prepare For > Discover how AI is transforming Medical Information, why regulators are increasing oversight, and how pharma companies can prepare for a future-ready MI operating model. *Medical Information is becoming one of the busiest and most strategically important functions in pharma. Artificial intelligence is accelerating that change, but the real challenge lies in how organisations adapt their operating models to keep pace.* Medical Information has quietly become one of the busiest functions in the pharmaceutical industry. It is also changing faster than many organisations are prepared for. Healthcare professionals and patients no longer expect to wait for a callback when they have a question about a product label, a dosing recommendation, or a potential side effect. They expect an answer immediately, through whichever channel is most convenient, and increasingly those expectations are shaped by the same AI-powered tools they use in everyday life. For companies responsible for Medical Information (MI), this shift is no longer something to prepare for in the future. It is already reshaping inquiry volumes, response expectations, and the level of regulatory scrutiny attached to every interaction. **Artificial intelligence is not changing the purpose of Medical Information. It is changing the expectations placed upon it.** ## In This Blog Post - Why Medical Information is becoming more complex - How AI is changing inquiry management - What regulators now expect from AI-enabled Medical Information - Why Medical Information and Pharmacovigilance must work more closely together - How pharma companies can prepare for a future-ready MI operating model ## Why Medical Information Is Under Increasing Pressure Three developments are reshaping Medical Information simultaneously. Each is manageable on its own. Together, they fundamentally change how MI functions need to operate. The first is the **rapid expansion of digital communication channels**. Medical inquiries no longer arrive primarily through telephone calls or email. They now originate from web forms, virtual assistants, chatbots, patient portals, and social media platforms. As a result, inquiry volumes have become less predictable and far less aligned with the traditional rhythms of a call centre. The second is **increasing scientific complexity**. New therapeutic areas, including GLP-1 therapies, Advanced Therapy Medicinal Products (ATMPs), and precision oncology treatments, generate questions that require increasingly specialised scientific and clinical expertise. The third is the **growing connection between Medical Information and Pharmacovigilance**. An adverse event or product quality complaint may surface through an MI inquiry just as easily as through a formal Pharmacovigilance reporting channel. Medical Information can therefore no longer be viewed as a stand-alone support function. Increasingly, it represents an important entry point into the broader product safety ecosystem. ## Regulators Are Preparing for AI in Medical Information Regulators are moving quickly to establish governance expectations for AI across the medicines lifecycle. In January 2026, the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA) jointly published ten guiding principles for Good AI Practice in medicine development. These principles explicitly address areas including: - pharmacovigilance - safety monitoring - clinical development - manufacturing Their recommendations focus on: - risk-based governance - data quality - lifecycle monitoring - transparency - meaningful human oversight Rather than introducing entirely new regulatory concepts, these principles reinforce expectations already emerging across Europe. They also provide the foundation for more detailed EU guidance currently under development. The EMA's earlier Reflection Paper on Artificial Intelligence in the Lifecycle of Medicines points in the same direction. It specifically identifies adverse event management and signal detection as areas where AI and machine learning can provide meaningful support, provided organisations maintain: - validated algorithms - documented intended use - explainable outputs - appropriate human oversight The message is consistent. **AI is welcome. Uncontrolled automation is not.** ## The MHRA Takes a Practical Approach The UK's Medicines and Healthcare products Regulatory Agency (MHRA) has adopted a complementary approach through its **AI Airlock** programme. Rather than focusing solely on guidance documents, the AI Airlock functions as a regulatory sandbox where AI-enabled healthcare technologies can be evaluated under real-world conditions before wider deployment. Insights emerging from the programme have highlighted several challenges that are directly relevant for Medical Information teams. These include: - AI hallucinations in generated content - validating AI-assisted responses - maintaining confidence in automatically drafted information - ongoing monitoring after deployment None of these findings suggest regulators intend to slow AI adoption. Instead, they reinforce a consistent principle that is now emerging across regulatory authorities. As AI becomes more deeply embedded within Medical Information and Pharmacovigilance, governance, traceability, validation, and documentation must evolve alongside it. ## What AI Means for Medical Information Teams For most in-house Medical Information functions, the challenge is becoming increasingly familiar. Do more. Respond faster. Handle greater scientific complexity. Maintain compliance. Often with the same resources. Artificial intelligence can genuinely support many of these activities. Examples include: - triaging incoming inquiries - identifying duplicate requests - suggesting draft responses - flagging content that requires updating after label changes - helping teams retrieve approved scientific information more efficiently These are meaningful opportunities to improve efficiency. They do not, however, remove regulatory accountability. Where an inquiry may contain a reportable adverse event, product quality complaint, or other safety-relevant information, regulators continue to expect that every decision remains documented, validated, traceable, and ultimately attributable to qualified human oversight. **The technology can accelerate the process. It cannot replace responsibility.** ## Why Operating Models Matter More Than Technology This is where organisational design becomes just as important as the AI solution itself. Companies evaluating AI within Medical Information should look beyond the technology and ask broader operational questions. For example: - Can our current operating model absorb increasing inquiry complexity? - Are Medical Information and Pharmacovigilance sufficiently integrated? - Can we demonstrate a complete audit trail for AI-assisted interactions? - Does every safety-relevant inquiry reach Pharmacovigilance without delay? - Are responsibilities clearly defined between people and technology? The answers to these questions often determine whether AI strengthens compliance or introduces new operational risk. One approach increasingly being adopted is a **tiered Medical Information model**. In this model: - routine product inquiries are managed through standardised workflows - more complex scientific questions are escalated to subject matter experts - any inquiry containing potential safety information is immediately routed to Pharmacovigilance Artificial intelligence can support each of these routing decisions. The final judgement, particularly where patient safety or regulatory compliance is involved, should remain under meaningful human oversight. That principle mirrors the broader direction regulators are taking across the medicines lifecycle. ## Medical Information and Pharmacovigilance Are Becoming Increasingly Connected Historically, Medical Information and Pharmacovigilance have often operated as closely related but separate functions. That distinction is becoming increasingly difficult to maintain. Every Medical Information interaction has the potential to reveal information relevant to product safety. A routine question about product use may contain: - an adverse event - a medication error - an off-label use - a product quality complaint As inquiry volumes increase and communication channels diversify, organisations need processes that ensure these signals are recognised consistently and escalated appropriately. Artificial intelligence can improve speed and consistency. It cannot replace the governance that ensures patient safety information reaches the right people at the right time. Increasingly, the organisations that perform best are those that treat Medical Information and Pharmacovigilance as **connected components of a single safety ecosystem** rather than as isolated operational functions. ## Preparing Your Medical Information Function for AI The direction of travel is becoming increasingly clear. European guidance on AI across the medicines lifecycle continues to evolve. National regulators are expanding practical experience through initiatives such as the MHRA AI Airlock. At the same time, expectations around governance, documentation, transparency, and human oversight continue to increase. Waiting for every detail of future guidance before taking action is itself a strategic decision. Organisations that begin strengthening their Medical Information operating model today will be significantly better positioned when regulatory expectations continue to mature. Preparing for AI does not begin with selecting a technology platform. It begins with building processes capable of using that technology safely, consistently, and compliantly. ### Key Takeaways - Medical Information is becoming increasingly complex as inquiry volumes, communication channels, and scientific content continue to evolve. - AI can improve efficiency across inquiry management, but it does not remove regulatory accountability. - Medical Information and Pharmacovigilance are becoming increasingly interconnected and should be governed accordingly. - Regulators consistently emphasise governance, transparency, validation, traceability, and meaningful human oversight. - Future-ready Medical Information functions invest in operating models as much as technology. ## Building a Future-Ready Medical Information Function Artificial intelligence is changing how Medical Information operates. Success will depend not only on adopting new technologies, but on building operating models that allow those technologies to be used safely, efficiently, and compliantly. QbD Group helps pharmaceutical companies strengthen their Medical Information capabilities through integrated expertise in: - Medical Information - Pharmacovigilance - Regulatory Affairs - Quality Management - AI governance - Process optimisation and operational excellence Whether you are exploring AI-enabled inquiry management, reviewing your Medical Information operating model, or strengthening the connection between Medical Information and Pharmacovigilance, our experts can help you build a scalable and inspection-ready approach. ## References - European Medicines Agency (EMA) / U.S. Food and Drug Administration (FDA) — [Guiding principles for good AI practice in medicine development (January 2026)](https://www.ema.europa.eu/en/news/ema-fda-set-common-principles-ai-medicine-development-0) - European Medicines Agency (EMA) — [Reflection paper on the use of artificial intelligence in the lifecycle of medicines](https://www.ema.europa.eu/en/news/reflection-paper-use-artificial-intelligence-lifecycle-medicines) - European Medicines Agency (EMA) — [Artificial intelligence (general page, roadmap and AI Observatory report)](https://www.ema.europa.eu/en/about-us/how-we-work/data-regulation-big-data-other-sources/artificial-intelligence) - UK Medicines and Healthcare products Regulatory Agency (MHRA) — [AI Airlock, the regulatory sandbox for AIaMD](https://www.gov.uk/government/collections/ai-airlock-the-regulatory-sandbox-for-aiamd) - UK Medicines and Healthcare products Regulatory Agency (MHRA) — [MHRA expands AI Airlock programme with a £3.6 million funding boost (April 2026)](https://www.gov.uk/government/news/mhra-expands-ai-airlock-programme-with-a-36-million-funding-boost-over-three-years) --- Source: https://qbdgroup.com/en/blog/ai-medical-information-pharma — © QbD Group. 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