Medical affairs teams are facing unprecedented pressure, not only from the proliferation of medical data and ever-expanding demands for engagement, but also from the ongoing challenge of proving their value to executives. The amount of data in healthcare is exploding, and medical science liaisons are taking on more of what was once handled by sales. In the United States, the ratio of sales reps to medical science liaisons (MSLs) has shrunk from 10:1 to 8:1, underscoring the central role medical affairs has become in helping doctors understand the science behind new treatments.
But persisting is daunting. Field reports, advisory boards, customer relationship management (CRM) notes, meeting minutes, and even social media generate thousands of signals that must be captured, validated, and contextualized. No one person or team can reasonably keep up.
Large language models (LLMs) like ChatGPT or Claude can quickly digest and summarize information, but they are still prone to hallucinations. In medicine, where misinformation can compromise patient safety and hinder diagnosis, maximizing accuracy is as important as maximizing speed.
Agentic AI offers a different approach. Rather than using a general model that generates a single response, agent AI introduces multiple specialized agents to function. Each tackles a narrow task—document monitoring, source verification, ontology markup, or compliance review—and then combines its results into a validated output.
Artificial intelligence agents have arrived at a critical moment in healthcare affairs, collaborating like a team of experts to verify, validate and contextualize medical information with unprecedented accuracy, transparency and personalization.
Improve accuracy
Without significant guidance to facilitate skills that most humans lack, general AI will not be able to reliably separate signal from noise. It can provide false or biased information with unwarranted confidence—dangerous in a medical setting.
Agent AI counters this by assigning dedicated agents to cross-check information against verified sources. For example, one person might check trial names and company attributes against ClinicalTrials.gov, another might flag unsubstantiated claims such as “safest” or “best,” and a third might review language for regulatory compliance—so every output is traceable and trustworthy.
Fighting prejudice
But when human bias sets in, even accurate information can be misinterpreted. Humans have cognitive biases that can distort medical evidence. It’s well known that for physicians, recency bias can make that last patient interaction or clinical case study more important than statistical evidence. A single negative side effect may inappropriately influence subsequent patient treatment decisions. General LLM can amplify these biases by learning from biased training data or showing users what they expect to see rather than what is most accurate.
Agent AI proactively combats this bias by validating across multiple sources and data sets. It contextualizes rare outliers in larger data sets and prevents overreaction to statistical outliers. For example, when a healthcare provider (HCP) observes a serious side effect, agent AI can immediately indicate that the side effect is less likely to occur in patients receiving treatment, helping to ensure that decisions are always based on evidence rather than anecdotes.
This balance is important. The Medical Affairs team confidently makes recommendations backed by thorough analysis rather than anecdotes, emotional reactions, or incomplete information. This evidence-based approach enhances trust between pharmaceutical companies and healthcare professionals.
Provide personalization
Medical affairs teams need insights beyond data summaries. Simple univariate analyzes can show what is happening but rarely explain why. They require understanding the complex, multiple relationships that connect the dots in ways that drive real-world medical outcomes. This enables trend and driver analysis and gets closer to helping teams understand the trajectory of their efforts on their impact on treatment patterns and patient outcomes. General AI may deliver one-size-fits-all content using outdated terminology that won’t resonate with professional audiences.
Agentic AI unifies evidence from sources targeting different audiences, including the opinions doctors express on the podium at scientific conferences with what they post on social media for patients, revealing relationships that might be missed by human review. By pairing agents that detect patterns with other agents that detect underlying drivers, it moves the analysis from correlation to explanation. With this deeper understanding, it acts like a team of medical experts conducting extensive research, allowing MSL to focus on other strategic efforts.
The same proxy framework also supports customized communications. Multiple agents can handle the same evidence but tailor the tone and language for different audiences. MSLs receive clinically precise summaries suitable for discussion with peers, while patient- or public-facing teams receive clear and accurate simple language explanations. This ensures messaging is consistent and compliant for each audience.
While today's traditional analysis relies primarily on frequency, or how often a topic occurs, as a proxy for importance, future proxy systems will move beyond this. They weigh information based on who said it, when and where it was said, and in what context it was said. In practice, a single insight from a key opinion leader on an advisory board may outweigh dozens of routine on-site mentions. As these information weighting mechanisms mature, medical affairs teams will gain clearer and more sophisticated insights, helping them make decisions based on impact rather than volume.
provide transparency
HCP needs explainable AI systems that can track and validate insights. In a regulated environment, professionals must understand not only the conclusions that AI reaches, but also how it reaches those conclusions.
As agent architectures evolve, they are expected to provide complete source attribution and verifiable reasoning chains for each output. Each professional agent will contribute to a transparent process that can be reviewed and confirmed by the medical team. This multi-layered design will ultimately bring together regulatory compliance, medical expertise and technical assurance such as Retrieval Enhanced Generation (RAG) to keep outputs based on trusted sources.
Trust depends on transparency. When Medical Affairs can show exactly how agent AI verifies each piece of data, they can enhance the trustworthiness of healthcare professionals. This strengthens professional relationships and ensures patient safety remains critical. In the early stages of AI adoption, reliable and evidence-based approaches are needed to avoid valid outputs being viewed as “fake” and to ensure that AI never replaces subject matter expertise.
The future of medical intelligence
Agent AI has the ability to facilitate medical affairs from reactive reporting to proactive strategies. As medical science grows exponentially, healthcare professionals will find it increasingly difficult to keep up with new research. MSLs and medical affairs teams have become even more important as trusted experts who can help physicians understand the science of treatment, but only if they have access to accurate, timely, verified information.
This shift is not just technological. In the age of misinformation, dedicated AI agents can ensure pharmaceutical companies operate with unprecedented accuracy and transparency when mobilizing evidence and science. These agents work together to build the trust that healthcare professionals and patients desperately need.
Intelligent AI will not replace medical expertise, but will enhance it. By handling validation, verification, and contextualization in the background, it allows medical professionals to focus on what they do best: improving patient outcomes through evidence-based care practices.
Photo: Lin Weiquan, Getty Images
Vic Ho is a distinguished medical affairs professional with more than 20 years of combined experience in field and strategic medical leadership positions. Prior to becoming global head of medical solutions at Sorcero, she served as global head of field medical communications for BMS Cardiovascular, head of medical capabilities and excellence at Jazz Pharmaceuticals, and consulted with a number of companies' medical affairs teams. Vic is known for his contributions in advancing healthcare strategy and point-of-care impact measurement, and is an active voice in the medical affairs community, driving optimization of insights management and promoting a customer- and patient-centric approach.
Seth Tyree is an experienced thought leader and strategic advisor specializing in the convergence of advanced data, analytics, and artificial intelligence to drive strategic decisions in pharmaceutical medical affairs. His comprehensive background includes deep expertise in life sciences and healthcare data, rigorous statistical analysis, business acumen and end-to-end product development. This powerful blend allows him to serve as a key translator, effectively connecting the strategic goals of medical affairs leaders with the technical execution of Sorcero’s AI implementation team. As Vice President of Customer Experience and Implementation, Seth is a trusted advisor and thought leader to clients, actively advising them on the design and implementation of complete healthcare insights programs—including strategy, people, process, data, and technology—to ensure they maximize value from their AI solutions and become more insights-driven.
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