By Jason Smith, CTO Within3
The dominant commercial AI narrative in 2026 is about scale. Larger models, broader capabilities, more parameters, and more tasks automated. Major platforms are positioning general-purpose language models as the natural fit for context-driven commercial and medical tasks, offering customers the ability to embed whichever frontier model they prefer into their existing workflows.¹ The implication is that capability scales with model size, and that access to the biggest, most general model available is the right answer for pharmaceutical organizations.
I want to make a precise argument against this assumption. Not because large models are useless in life sciences, they are not, but because “bigger” is the wrong evaluation criterion for the specific problem pharma organizations are trying to solve.
The Problem Is Not Generation Quality. It Is Fidelity.
When a Medical Affairs or Commercial Insights team asks an AI system to synthesize twenty advisory board transcripts into a structured analysis of unmet patient need, competitive positioning, or market access barriers – the output quality question is not “Is this well-written?” It is “Is this true?”
These are different questions, and large general-purpose models are optimized for the former. They are extraordinarily good at producing fluent, coherent, contextually plausible text. They are less consistently reliable at distinguishing between what an expert actually said in a transcript and what a plausible expert might have said on that topic, given the language patterns in their training data. The gap between “contextually plausible” and “factually accurate relative to source material” is small in casual use cases, but consequential in regulated ones.
The European Medicines Agency (EMA) and Heads of Medicines Agencies (HMA) have named this problem directly. Their published guidance on large language models in medicines regulation identifies hallucination as a primary risk and flags the inadequacy of overreliance on AI-generated outputs without independent verification.² Peer-reviewed research reaches the same conclusion: evaluation approaches for healthcare language models are inconsistent, factual error rates in complex clinical and scientific content are non-trivial, and failure modes are not reliably predictable from model size or general benchmark performance.
The FDA frames the relevant evaluation principle as credibility per context of use. A model that performs well on a general benchmark is not thereby credible for a specific medical affairs synthesis task. Credibility requires evaluation against the actual task, with the actual data types, using standards that the organization can define and defend.³
This is a meaningful distinction. It means the evaluation discipline required to deploy AI responsibly in pharma is not a checkbox on a procurement form. It is an ongoing organizational capability.
What Domain Fit Actually Requires
There are five capabilities that separate a domain-fit model evaluation approach from simply embedding whatever frontier model is currently popular.
The first is a model evaluation harness: an automated testing infrastructure that can run a candidate model against a defined set of tasks and measure its outputs against a ground truth. This requires real investment to build, but it is the only basis for a defensible claim that a model performs reliably on a specific use case. Without it, organizations are operating on assumptions.
The second is domain test sets: curated collections of representative tasks drawn from actual medical and commercial workflows, with documented gold-standard answers. A test set for medical affairs synthesis looks different from one for commercial competitive intelligence, and neither resembles a general Natural Language Processing (NLP) benchmark. The tasks are different, the error types that matter are different, and the definition of “correct” is different in each domain.⁵⁴
The third is retrieval-grounded outputs with citations. A model that generates a synthesis without tracing each claim to a specific source document cannot be audited and cannot be trusted in a regulated environment. Grounding outputs in retrieved source material and surfacing explicit citations is a deliberate architectural choice, not a default behavior of general-purpose models. It is also the single most important mitigation against hallucination in high-stakes synthesis tasks.²
The fourth is terminology normalization. Life sciences language is highly specialized and inconsistently used across organizations, therapeutic areas, and functions. A model that generates outputs using inconsistent terminology, even if the underlying meaning is technically correct, creates friction in every downstream workflow that depends on structured tagging, routing, or aggregation. Over time, terminology drifts into data quality problems that are expensive to remediate.
The fifth is multilingual capability with documented evaluation. Global pharmaceutical organizations run advisory programs across multiple languages. The accuracy and consistency of translation are not a secondary concern. General-purpose machine translation quality assumptions are not sufficient for content where nuance in scientific language is material to the insight.
The Benchmark Argument
When buyers push back on domain-fit evaluation with the argument that a frontier model’s general benchmark performance is sufficient justification, the right response is a simple question: Which benchmark? And what was the task?
General language model benchmarks test reasoning, knowledge, and language understanding across broad domains. They do not test the accuracy of scientific claim synthesis against a specific set of source transcripts. They do not test terminology consistency in a given therapeutic area. They do not test the false negative rate on adverse event signal detection in a medical affairs engagement context. They do not test whether competitive shifts or access barriers were accurately captured from a commercial advisory board.
The FDA’s framework is unambiguous on this point. Credibility assessment is context-specific. A model that scores well on a general benchmark but has never been evaluated on the specific decision-support task it is being asked to perform has not been assessed. It has been assumed.³
The correct benchmark for a pharma insights AI system is the reviewer correction rate – how often a qualified medical affairs or commercial professional must fix its outputs when checked against the source material. It is the hallucination rate in scientific claim synthesis, or the fidelity in capturing market access barriers, competitive positioning, or customer sentiment. It is accuracy and consistency against a defined gold standard for the specific task types the system is deployed on.
Measuring those metrics requires a structured evaluation program, an internal protocol that defines what “correct” means per task type, and drift monitoring as the therapeutic area landscape evolves. That is a real investment. It is also the investment that allows an organization to say, with evidence rather than assumption, that its AI produces trustworthy outputs for the specific decisions it supports.
Fewer Wrong Decisions Is the Differentiator
The productivity argument for AI in life sciences is well understood. AI accelerates content generation, reduces manual effort in report assembly, and compresses the time from data collection to insight delivery. These are real benefits.
But AI-generated insight that is wrong creates a different kind of cost. A forecast adjustment made on the basis of a hallucinated synthesis finding. A competitive response triggered by a misattributed HCP opinion. A publication strategy shaped by a summary that conflated two separate expert perspectives. These are not theoretical failure modes. They are the predictable consequences of deploying general-purpose models on specialized tasks without domain-specific evaluation.
The differentiation that matters in this space is not “more content, faster.” It is “fewer wrong decisions, with evidence.” That positioning requires a credible evaluation program, benchmarks against domain-specific task sets, and transparency to be honest about what a model does not do well, alongside what it does well.
That transparency, backed by evidence, is what earns trust in a regulated environment. And trust is the prerequisite for scale. The industry’s best AI deployments in medical affairs and commercial insights will not be distinguished by which model they used. They will be distinguished by whether they built the evaluation infrastructure to know whether it was working. Platforms that make this evaluation discipline central to their architecture reflect where the field needs to go. Within3 is building toward this future and designing evaluation infrastructure and domain-fit architecture as foundational, not optional, to how pharma AI should work.
References
European Medicines Agency / Heads of Medicines Agencies. “Harnessing AI in Medicines Regulation: Use of Large Language Models (LLMs).” https://www.ema.europa.eu/en/news/harnessing-ai-medicines-regulation-use-large-language-models-llms
U.S. Food and Drug Administration. “Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products.” https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological
Published evidence on evaluation inconsistency in healthcare LLM deployments. JAMA. https://jamanetwork.com/journals/jama/fullarticle/2825147
Veeva Systems. “Veeva Announces AI in Vault CRM.” https://ir.veeva.com/news/news-details/2024/Veeva-Announces-AI-in-Vault-CRM/default.aspx
Within3. Insights Management Platform. https://within3.com/within3-insights-management-platform-details
