How Do You Evaluate Generative AI in Pharma? 2026 Guide

Short answer: Evaluate the whole system in its real use, not the model on a public leaderboard. Start from the business goal, break the workflow into steps, pick domain metrics for each step, and test against a realistic dataset that includes edge cases. Then keep evaluating after launch, because generative AI is probabilistic and its behaviour drifts. Regulators in the EU and US now expect exactly this: a defined context of use, risk-based performance assessment and lifecycle monitoring.

Key takeaways

• AI models are improving faster than the benchmarks used to measure them, and test data leaking into training can inflate scores (Stanford HAI AI Index, 2026).
• In medicine, only 5% of studies in a review of more than 500 LLM evaluations used real clinical data (AI Index 2026, medicine chapter).
• In a 2025 study of clinical note summarisation, 1.47% of generated sentences were hallucinations, and 44% of those could have affected patient care (npj Digital Medicine, 2025).
• The EMA and FDA's January 2026 principles call for risk-based performance assessment of the complete system and scheduled re-evaluation over its life cycle (EMA).
• Filip Rais, Director of Technical Architecture at MSD, told our Pharma Meets AI conference: "The risk isn't that AI fails. The risk is that you won't know when it does."

Why don't public AI benchmarks tell you enough?

Because they measure the model, not your use of it. Public benchmarks are useful as a rough guide, but three problems limit them. They saturate: top models now score so high that tests stop telling them apart. They can be contaminated: if test questions end up in a model's training data, the score overstates its ability. And they are generic: a high score on maths or coding questions says little about how a system will summarise a safety case or screen scientific literature.

The 2026 AI Index from Stanford HAI found AI capability is "outpacing the benchmarks designed to measure it". Its medicine chapter made the same point for healthcare: LLMs have passed most clinical reasoning benchmarks, but strong scores "have yet to translate reliably to measurable clinical outcomes".

What did we hear on our stage?

At the 2nd Pharma Meets AI conference in Barcelona in April 2026, Filip Rais, Director of Technical Architecture at MSD, gave a practical guide to evaluating generative and autonomous AI in regulated industries. He called the problem the "benchmark illusion", and his conclusion was blunt:

"No combination of public benchmark scores can reliably predict model performance!"
Filip Rais, MSD, 2nd Pharma Meets AI, April 2026

The theme of his presentation was that what matters is how the whole system behaves, not the model or a single metric. As he put it: "In AI systems, the evaluation is the product."

Why is generative AI harder to validate than traditional software?

Traditional pharma software is deterministic. The same input gives the same output, so validation works like an inspection: test once, sign off, move on. Generative AI is probabilistic. Outputs vary, so teams measure confidence, accuracy and hallucination rates, compare outputs for similarity, and use human ratings or another model as a judge.

Rais linked technical failures to the consequences that matter in pharma. Hallucination, bias and opacity can lead to patient harm, off-label promotion, discrimination or regulatory non-compliance. In his words: "The regulator doesn't care about the cause - they care about the impact." So evaluation should test for what the company will be held accountable for, not only for what engineers call the problem.

The risk is measurable. In a 2025 npj Digital Medicine study of LLM-generated clinical summaries, 1.47% of sentences contained hallucinations and 3.45% of transcript sentences were omitted. Of the hallucinations, 44% were classed as major, meaning they could affect patient care.

How should pharma teams evaluate a generative AI system?

Rais set out a three-part approach, adapted from the Goal, Question, Metric method used in software engineering:

1. Start from the goal and break it down. Define what the user needs, split the workflow into actions, and choose metrics for each step and for the end-to-end result. For a scientific literature review, that could mean recall when finding candidate papers, relevance and diversity of the papers selected, and correct expansion of citations.
2. Build the right evaluation dataset. Check its scope (which tasks, edge cases and failure modes it covers), its source (expert-annotated golden data, real-world data or synthetic data) and its scale (enough signal to draw defensible conclusions). His shorthand: "Garbage In, Disaster Out!"
3. Scale and automate. Treat evaluation as infrastructure that runs continuously, not a one-time checkpoint, and track its cost. As he warned: "If you don't measure tokens, you're not scaling evaluation — you're scaling spend."

What are the most common evaluation mistakes?

Rais named several traps:

• Isolation fallacy: testing the model on its own instead of the full pipeline in its real setting.
• Vibe check fallacy: spot-checking a few outputs instead of evaluating systematically.
• Happy path bias: skipping edge cases, adversarial inputs and negative constraints.
• Golden data fallacy: testing a changing system against a static dataset.
• Benchmark contamination: testing on data the model may have seen in training.
• LLM-as-a-judge fallacy: relying on AI judges that are themselves brittle and biased.
• Over-engineering: building evaluation suites more complex than the system they test.

What do regulators expect?

The direction is the same on both sides of the Atlantic: define the context of use, match the evidence to the risk, and keep monitoring.

• EMA and FDA, January 2026: the ten guiding principles of good AI practice in drug development include risk-based performance assessment that evaluates "the complete system including human-AI interactions", and life cycle management with scheduled monitoring and periodic re-evaluation.
• FDA, January 2025 draft guidance: a seven-step, risk-based credibility assessment for AI used to support regulatory decisions on drugs. Model risk combines model influence and decision consequence. As of October 2026 it is still a draft.
• EU AI Act: high-risk AI systems must achieve appropriate accuracy, robustness and cybersecurity throughout their life cycle, declare their accuracy metrics in the instructions for use (Article 15), and have post-market monitoring (Article 72). Under Regulation (EU) 2026/1744, these rules apply to stand-alone high-risk systems from 2 December 2027.

For how governance helps AI move from pilot to production, see Why Do Most Pharma AI Projects Never Leave the Pilot Stage?.

What this means for pharma AI teams

• Stop shopping by leaderboard. Use public benchmarks to shortlist models, then test on your own tasks.
• Define the context of use first. What decision does the output support, and what happens if it is wrong?
• Measure business-relevant failures. Test for off-label claims, omissions and bias, not only accuracy.
• Invest in evaluation data. Expert-annotated examples and edge cases are as valuable as the model.
• Monitor in production. Track performance, drift and cost continuously, and document it for inspectors.
• Keep a human accountable. Decide who reviews outputs and when the system must hand over to a person.

What to watch in 2027

• FDA final guidance: whether the January 2025 credibility framework is finalised, and new CDER AI drafts.
• EU AI Act deadlines: preparation for the December 2027 high-risk obligations, and new harmonised standards on accuracy and robustness.
• Evaluation of AI agents: methods for testing multi-step, multi-agent systems, which Rais called hard to evaluate because of the many possible step sequences.
• Clinical-grade benchmarks: more evaluations built on real clinical and regulatory data instead of exam-style questions.

Sources

1. Rais, F. (2026). Evaluating Generative & Autonomous AI Systems in Regulated Industries. Presentation at the 2nd Pharma Meets AI conference, UBIQ Events, Barcelona, April 2026.
2. Stanford HAI. (2026). AI Index Report 2026, Chapter 2: Technical performance. hai.stanford.edu (accessed 23 October 2026)
3. Stanford HAI. (2026). AI Index Report 2026, Chapter 6: Medicine. hai.stanford.edu (accessed 23 October 2026)
4. Asgari, E., et al. (2025). A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation. npj Digital Medicine. nature.com (accessed 23 October 2026)
5. European Medicines Agency and US Food and Drug Administration. (2026). Guiding principles of good AI practice in drug development. ema.europa.eu (accessed 23 October 2026)
6. US Food and Drug Administration. (2025). Considerations for the use of artificial intelligence to support regulatory decision-making for drug and biological products (draft guidance). fda.gov (accessed 23 October 2026)
7. Regulation (EU) 2024/1689 (Artificial Intelligence Act), Articles 15 and 72. eur-lex.europa.eu (accessed 23 October 2026)

Header photo: Diana Hauan on Unsplash.