Medical affairs teams are under pressure like never before, not only from the surge of healthcare data and expanding engagement demands, but from the constant challenge of proving their value to the C-suite. Data volumes in healthcare have exploded, while medical science liaisons are taking on more of the engagement once handled by sales. That ratio of sales reps to Medical Science Liaisons (MSLs) has narrowed from 10:1 to 8:1 in the U.S., underscoring how central medical affairs has become in helping physicians understand the science behind new therapies.
But keeping up is daunting. Field reports, advisory boards, Customer Relationship Management (CRM) notes, congress proceedings, and even social media generate thousands of signals that must be captured, validated, and contextualized. No single person or team can reasonably keep pace.
Large Language Models (LLMs) like ChatGPT or Claude can digest and summarize information quickly, yet they remain prone to hallucination. In medicine, where misinformation can risk patient safety and hinder diagnosis, maximizing accuracy matters as much as increasing speed.
Agentic AI offers a different approach. Instead of one general-purpose model generating a single response, agentic AI brings several specialized agents into play. Each handles a narrow task — literature monitoring, source verification, ontology tagging, or compliance review — before their results are combined into one validated output.
AI agents have arrived at a critical moment for medical affairs, collaborating like an expert team to validate, verify, and contextualize medical information with unprecedented accuracy, transparency, and personalization.
Enhancing accuracy
General-purpose AI can’t reliably separate signal from noise without significant guidance in prompting a skill most people lack. It may present false or biased information with unwarranted confidence — dangerous in a medical setting.
Agentic AI counters this by assigning specialized agents to cross-check information against verified sources. For example, one might check trial names and company attributions against ClinicalTrials.gov, another flags unsubstantiated claims like “safest” or “best,” and a third reviews language for regulatory compliance — so every output is traceable and trustworthy.
Countering bias
But even accurate information can be misinterpreted when human bias enters the picture. Humans have cognitive biases that can distort medical evidence. It is well known that for doctors recency bias can make the last patient interaction or clinical case study feel more significant than statistical evidence. A single negative side effect can inappropriately influence treatment decisions for subsequent patients. General purpose LLMs can amplify these biases by learning from biased training data or showing users what they expect to see rather than what’s most accurate.
Agentic AI actively counters this bias by validating across multiple sources and datasets. It contextualizes rare outliers within larger datasets, preventing overreaction to statistical outliers. For example, when an Health Care Provider (HCP) observes one severe side effect, agentic AI can immediately show that it represents a low probability across treated patients, helping ensure that decisions remain anchored in evidence, not anecdote.
That balance matters. Medical affairs teams present recommendations with confidence, backed by comprehensive analysis rather than anecdote, emotional reactions, or incomplete information. This evidence-based approach strengthens trust between pharma companies and healthcare professionals.
Delivering personalization
Medical affairs teams need insights that go beyond data summaries. Simple, univariate analyses can show what is happening but rarely explain why. They require an understanding of complex, multivariate relationships that connect the dots in a way that drives real-world medical outcomes. This enables trend and driver analysis and gets closer to helping teams see the trajectory of their effort toward impact on treatment patterns and patient outcomes. General-purpose AI may deliver one-size-fits-all content using outdated terminology that doesn’t resonate with specialized audiences.
Agentic AI unifies evidence from sources aimed at different audiences, Opinion expressed by Drs at the podium of a scientific congress vs what they post for their patients on social media, revealing relationships that a manual review might miss. By pairing agents that detect patterns with others that detect potential drivers, it moves analysis from correlation to explanation. With this deeper level of understanding it functions like a team of medical experts performing extensive research, freeing MSLs to focus on other strategic work.
The same agentic framework also enabled tailored communication. Multiple agents can process the same evidence, but adapt the tone and language for different audiences. MSLs receive clinically precise summaries suited for discussions with peers, while patient- or public-facing teams get plain language explanations that are both clear and accurate. This ensures consistent and compliant messaging across every audience.
And while today traditional analytics rely mostly on frequency or how often a topic appears as a proxy for importance, future agentic systems will go beyond that. They will weigh information based on who said it, when and where it was said and in what context. In practice a single insight from a key opinion leader on an advisory board might outweigh dozens of otherwise routine field mentions. As these mechanisms for information weighting mature, medical affairs teams will get clearer, sophisticated insights that help them make decisions grounded in influence, not volume.
Providing transparency
HCPs need explainable AI systems where insights can be traced and verified. In regulated environments, professionals must understand not just what the AI concludes, but how it reached those conclusions.
As agentic architectures evolve, they’re expected to deliver full source attribution and a verifiable chain of reasoning for every output. Each specialized agent will contribute to a transparent process that medical teams can audit and confirm. This multi-layered design will ultimately weave together regulatory compliance, medical expertise, and technical safeguards such as retrieval augmented generation (RAG) to keep outputs grounded in trusted sources.
Trust depends on transparency. When medical affairs can show exactly how agentic AI validated each piece of data, they strengthen their credibility with health care professionals. This reinforces professional relationships and ensures patient safety remains paramount. In these early stages of AI adoption, credible and evidence-based methodologies will be necessary to avoid valid outputs being dismissed as “fake” and ensure that AI never substitutes for subject matter expertise.
The future of medical intelligence
Agentic AI has the capability to catalyze medical affairs from reactive reporting to proactive strategy. As medical science accelerates exponentially, HCPs will find it increasingly difficult to keep current with new research. MSLs and medical affairs teams become even more critical as trusted experts who help physicians understand treatment science — but only if they have access to accurate, timely, validated information.
This shift is more than technological. In an era of misinformation, specialized AI agents can ensure that when pharma companies mobilize evidence and science, they can do so with unprecedented accuracy and transparency. Working together, these agents create the trust that healthcare professionals and patients desperately need.
Agentic AI doesn’t replace medical expertise — it amplifies it. By handling validation, verification, and contextualization in the background, it frees medical professionals to focus on what they do best: improving patient outcomes through the practice of evidence-based care.
Photo: Weiquan Lin, Getty Images
Vic Ho is a distinguished Medical Affairs professional with over 20 years combined experience in Field and Strategic Medical leadership roles. Before becoming the Global Medical Solutions Lead for Sorcero she held positions as WorldWide Field Medical Communications Lead for Cardiovascular at BMS and Head of Medical Capabilities and Excellence at Jazz Pharmaceuticals as well as consulting for many companies Medical Affairs teams. Vic is known for her contributions to advancing medical strategy and field medical impact measurement and is an active voice in the Medical Affairs community driving optimization of insights management and fostering customer and patient focused approaches.
Seth Tyree is a seasoned thought leader and strategic advisor specializing in the convergence of advanced data, analytics, and AI to drive strategic decision-making within Pharmaceutical Medical Affairs. His comprehensive background includes deep expertise across life sciences and healthcare data, rigorous statistical analysis, commercial business acumen, and end-to-end product development. This powerful blend allows him to serve as a critical translator, effectively bridging the strategic goals of Medical Affairs leaders with the technical execution of AI implementation teams at Sorcero. As VP of Customer Experience and Implementations, Seth serves as a trusted advisor and thought leader for his customers, actively advising them on designing and operationalizing complete medical insights programs — including strategy, people, process, data, and technology — to ensure they maximize value from AI solutions and become more insights-driven.
This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.
