Perspectives from the 2026 European Meeting of ISMPP session titled “Faster? Better? Cheaper? Debating the Value Proposition of AI in Medical Communications”
Introduction
At the 2026 European Meeting of ISMPP, a lively “balloon debate” explored a deceptively simple question: what is AI’s most important value proposition in medical communications?
Framed around the familiar tension of delivering work faster, better, or cheaper, the session invited attendees to consider whether AI might fundamentally change the tradeoffs that medical communications teams have long managed. Presented in a deliberately provocative debate format, four speakers defended different perspectives on AI’s value: quality and trust, speed and scalability, transformational possibility, and informed skepticism.
While the positions were intentionally sharpened for discussion, the conversation itself revealed something more nuanced. Across viewpoints, several shared themes emerged around trust, human oversight, scalability, and the growing complexity of scientific communication. By the final round of debate, the speakers themselves acknowledged that the positions were less oppositional than they initially appeared.
Theme 1: Quality as the Foundation of Trust — with Speed as a Conditional Enabler
Jason Gardner (Real Chemistry) argued that AI’s greatest value lies in strengthening quality, accuracy, and consistency across medical communications. In healthcare, he noted, quality is not simply a desirable outcome; it underpins trust, credibility, and patient safety. Even minor inconsistencies across publications, slide decks, or educational materials can have meaningful consequences.
Against a backdrop of rapidly expanding medical literature and increasing cognitive burden on teams, AI was framed as a mechanism for supporting scientific rigor rather than replacing human expertise. Gardner described AI as augmenting human thinking by helping teams synthesize large volumes of information, identify inconsistencies, and maintain alignment across multiple deliverables. Importantly, he repeatedly emphasized the need for “human oversight” and high-quality inputs.
Quality isn’t a nice-to-have. In healthcare, quality is safety, trust, and the license for us to operate. – Jason Gardner
Anisha Mehra (Ferring Pharmaceuticals) approached the debate from a different angle, focusing on AI’s ability to accelerate evidence generation, insight identification, content creation, and global deployment. Through a staged interaction with an AI-generated “virtual colleague,” she illustrated how AI tools could reduce manual workload and create operational scale within medical affairs teams.
Examples included AI-assisted literature reviews, automated insight clustering, and modular content approaches that could support adaptation of global materials for local markets and audiences. Rather than positioning AI as a replacement for medical writers, Mehra described it as shifting professionals toward higher-value work such as interpretation, strategic review, and scientific engagement.
Although Gardner and Mehra emphasized different benefits, their perspectives converged on a common theme: AI can help teams manage growing complexity while preserving trust in medical communications. For Gardner, that trust begins with quality, accuracy, and human oversight. For Mehra, AI-enabled speed allows teams to generate insights, adapt content, and respond to evolving needs more effectively. Together, these perspectives suggest that speed delivers value when it supports—not supersedes—the quality standards that underpin scientific credibility.
Theme 2: AI as an Enabler of New Possibilities
Martin Delahunty (Inspiring STEM Consulting) argued that the most important impact of AI may not be doing existing work faster, but enabling work that was previously impractical to deliver at scale.
Drawing on Eric Topol’s book Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, Delahunty connected developments in clinical AI with opportunities in medical communications. He referenced examples such as AI-powered clinical scribes, which can reduce documentation burden and allow clinicians to spend more time interacting directly with patients. The parallel for medical communicators, he suggested, is the opportunity to reduce repetitive production work and create more space for strategic thinking, scientific interpretation, and meaningful audience engagement.
This perspective focused heavily on accessibility and reach. AI could make it more practical to create materials that are often deprioritized because of resource constraints: patient-friendly summaries, region-specific educational resources, plain-language explanations, and materials adapted for different literacy levels or healthcare settings.
Delahunty also highlighted AI’s potential to help teams maintain “currency” with an increasingly unmanageable evidence base. Rather than relying solely on periodic literature synthesis, AI tools may help continuously monitor publications, identify emerging patterns, and surface insights across large datasets.
The gift of the machine is not that it works harder than we do. It’s that it frees us to be more deeply human in our work. – Martin Delahunty
Importantly, this argument was not presented as technology replacing human contribution. Instead, AI was framed as expanding the scope of what medical communications teams can realistically support. As Delahunty noted during the debate, the opportunity lies not simply in efficiency gains, but in creating scientific communications that previously would not have been practical to produce.
Theme 3: The Importance of Selective and Critical AI Adoption
Daniel Heuman (Intelligent Editing Ltd) introduced a deliberately skeptical perspective that added an important counterpoint to the discussion. His argument was not anti-AI; rather, it centered on the idea that AI’s value depends on understanding where it works well, where it does not, and how carefully it is applied.
Central to this viewpoint was the “Person in the Box” framework, which encourages teams to evaluate workflows task by task. Some activities, Heuman argued, are highly suitable for AI support, particularly small, clearly bounded tasks. Others become problematic when complexity, ambiguity, or contextual judgment increase.
He illustrated this distinction with a simple example: asking AI to rewrite a single sentence in plain language may work extremely well, while asking it to simplify an entire document introduces far greater variability and risk. He described this tendency to overextend AI into overly broad applications as the “big box blunder.”
The people who use AI best are not the ones who think it can do everything. They’re the ones who apply it selectively.
– Daniel Heuman
This perspective also raised broader concerns surrounding AI adoption, including data security, implementation burden, environmental impact, copyright considerations, and the downstream workload associated with reviewing low-quality AI outputs. Several comments during the debate focused on the danger of over-trusting AI-generated summaries or deliverables without careful human review.
At the same time, Heuman acknowledged that AI can provide meaningful value when used deliberately and with strong oversight. His emphasis on “healthy cynicism” ultimately reinforced, rather than contradicted, themes raised by the other speakers. Across the discussion, human judgment consistently emerged as essential.
Closing Insight
Although one perspective ultimately “won” the balloon debate, the session resisted a simple conclusion. The four viewpoints collectively revealed three interconnected themes: AI’s ability to support trust and quality, its potential to expand what medical communications teams can accomplish, and the importance of applying it with discernment and oversight.
Rather than positioning speed, innovation, and skepticism as opposing forces, the discussion suggested that they are increasingly interdependent. As AI adoption continues across medical affairs and communications, the central challenge may be less about choosing a single value proposition and more about balancing these priorities responsibly in service of accurate, accessible, and meaningful scientific exchange.
Acknowledgment: The ISMPP Insider Content Lab team would like to thank the four faculty volunteers and session moderator who participated in the 2026 European Meeting of ISMPP, Faster? Better? Cheaper? Debating the Value Proposition of AI in Medical Communications.
Disclaimer: The views and opinions expressed by the speakers in their presentation at the 2026 European Meeting of ISMPP are their own and do not necessarily reflect the views, policies, or positions of their employer or of ISMPP.
Disclosure: This article was developed by the ISMPP Insider Content Lab team using ChatGPT 5.2 (OpenAI) customized to meet the requirements for a Content Lab article. Inputs to the custom GPT were the ISMPP-supplied session transcript (Snapsight) and the onsite slide presentation by the faculty. Human editorial and process oversight was provided by members of the Content Lab team, Vidhi Vashisht, Jerolyn Monte, and Ross Ruriani, and Doreen Valentine (ISMPP).
©2026 International Society of Medical Publication Professionals (ISMPP)
