ClinReg Benchmark?: What They Mean for Regulatory and Clinical Professionals

Artificial Intelligence (AI) is rapidly transforming pharmaceutical research and development. While most AI benchmarks have traditionally focused on drug discovery, there has been limited evaluation of AI's performance in regulatory submissions, pharmacovigilance, and clinical operations. To address this gap, the ClinReg Benchmark was developed as a public benchmark designed specifically to assess how effectively Large Language Models (LLMs) perform real-world regulatory and clinical tasks within the pharmaceutical industry.

Why Was the ClinReg Benchmark Created?

The pharmaceutical industry operates in a highly regulated environment where accuracy is essential. Even a minor AI-generated error—such as missing an important safety publication or producing incorrect regulatory information—can affect regulatory submissions, patient safety, and overall compliance.

The ClinReg Benchmark was created to determine whether today's AI models are reliable enough to support regulated pharmaceutical workflows while maintaining the level of accuracy expected in clinical and regulatory environments.

Purpose of the ClinReg Benchmark

The benchmark focuses on evaluating AI performance across real pharmaceutical workflows by:

• Measuring AI accuracy in clinical, pharmacovigilance, and regulatory activities.

• Comparing open-weight AI models with proprietary (closed-source) AI models.

• Assessing whether AI can safely support high-risk regulatory processes.

Applications Across Regulatory and Clinical Functions

As reasoning capabilities continue to improve, open-weight LLMs can support a wide range of regulatory and clinical activities, including:

• Regulatory intelligence and guidance summarization

• Clinical protocol and study document review

• Medical writing support

• Safety data analysis and signal evaluation

• Literature surveillance

• Labeling and lifecycle management

• Preparation of regulatory documentation

These applications have the potential to improve efficiency while reducing repetitive manual activities across the product lifecycle.

Three Real-World Pharmaceutical Tasks Evaluated

1. Literature Screening

Literature screening is a critical activity across Clinical Research and Pharmacovigilance. The benchmark evaluates an AI model's ability to screen published scientific literature and determine whether an article should be included or excluded based on predefined criteria.

This closely resembles activities such as:

• Pharmacovigilance literature monitoring

• Signal detection

• Systematic literature reviews

• Post-marketing surveillance

Accurate literature screening is essential because missing relevant safety publications can directly impact benefit-risk assessments and regulatory decision-making.

2. IND Module 3 Authoring

The second task focuses on authoring Module 3 (Chemistry, Manufacturing & Controls - CMC) of an Investigational New Drug (IND) application.

The benchmark evaluates whether AI can:

• Extract CMC information accurately

• Draft Module 3 documentation

• Avoid hallucinating unsupported information

• Prevent omission of important regulatory details

This task closely reflects the document preparation activities routinely performed by Regulatory Affairs professionals.

3. TLF Generation (Tables, Listings & Figures)

The third task evaluates AI's ability to generate Tables, Listings, and Figures (TLFs) from clinical trial datasets.

The benchmark measures whether AI can correctly convert clinical data into standardized outputs commonly used in:

• Biostatistics

• Statistical Programming

• Clinical Data Management

These outputs form an essential component of Clinical Study Reports (CSRs) and regulatory submissions submitted to health authorities.

How Were the AI Models Evaluated?

Rather than measuring only overall accuracy, the ClinReg Benchmark evaluates several performance indicators, including:

• Overall task accuracy

• Structured data extraction accuracy

• Fabrication (hallucination) rate

• Omission rate

• Ability to detect and correct errors

• Cost of completing each task

• Reliability across multiple runs

These evaluation criteria provide a more realistic assessment of AI performance in regulated pharmaceutical environments.

Key Findings

The benchmark produced several important findings for the pharmaceutical industry:

• Leading open-weight AI models performed almost as accurately as proprietary AI models.

• Several open-weight models achieved similar performance while operating at significantly lower cost.

• AI models demonstrated different error patterns:

  • Some models generated fabricated (hallucinated) information.

  • Others omitted information that was available in the source documents.

• High overall accuracy alone does not guarantee that a model is appropriate for regulatory applications. Understanding a model's error profile is equally important.

Future Outlook

The ClinReg Benchmark represents an important milestone in evaluating AI for pharmaceutical regulatory, clinical, and safety applications. Its findings suggest that modern AI models are becoming capable of supporting many regulated workflows, particularly in document preparation, literature screening, and clinical data processing.

However, the benchmark also reinforces an important principle: AI should augment—not replace—human expertise. Human review, validation, and regulatory oversight remain essential before AI-generated content is incorporated into regulated submissions. Future AI adoption in the pharmaceutical industry will depend not only on model accuracy, but also on transparency, validation, and the ability to understand how AI behaves when information is incomplete or uncertain.

References

• Open-Weight LLMs Have Caught Up on Reasoning