How AI Is Transforming Drug Labeling

Introduction

The pharmaceutical industry faces a persistent operational challenge: extracting critical information from thousands of complex drug labels every year. These documents contain essential safety data, dosing instructions, adverse reactions, and drug interactions that regulatory teams, safety professionals, and clinical decision-makers urgently require to be accessed quickly and accurately.

Traditional manual review is slow and labor-intensive. A regulatory professional might spend two to three days reviewing a single label to identify all mentions of drug interactions or safety concerns. The presence of tens of thousands of active drug labels in circulation creates a significant operational bottleneck.

Recent breakthroughs in artificial intelligence offer a practical solution. Specialized AI language models—systems trained specifically on drug label data—can now automatically extract this critical information in minutes instead of days.

This article explains how these tools function, why they are essential, and how pharmaceutical professionals can implement them.

The Core Problem: Generic AI Systems Don't Understand Drug Labels

To understand why specialized AI models, matter, it helps to know how standard AI systems work. Models such as BERT (a widely used language AI) are trained on vast amounts of general English text: Wikipedia articles, news stories, books, and websites. This makes them excellent at understanding everyday language.

But drug labels are fundamentally different from everyday language. They contain complex dosing instructions, such as "In patients with severe liver impairment, the recommended dose is 50 mg once daily" versus "In patients with mild-to-moderate liver impairment, dose adjustment may not be necessary."

To a general AI system, these statements appear similar. To pharmaceutical professionals, one represents an absolute restriction, while the other indicates a conditional adjustment. Generic AI systems often miss these critical distinctions.

Drug labels also feature regulatory standardized language mandated by FDA guidelines (21 CFR Part 201), precise pharmaceutical vocabulary, technical specifications (like "mcg/mL" or "mg/kg/day"), and complex information about how drugs are absorbed, distributed, metabolized, and eliminated from the body—a process abbreviated as ADME.

Researchers assessed how standard AI models understood diverse types of text and found that, when visualizing how AI systems clustered text types, drug labels formed a distinct cluster separate from clinical notes, medical abstracts, and general English text.¹ This proved to be a critical point: drug labeling is a distinct linguistic domain, and models not trained explicitly on it struggle with it.

Real-world performance confirmed this limitation. When BioBERT, a model trained in general biomedical literature, was evaluated on drug indication extraction from FDA labels, it achieved an F1 Score of 0.908—good but not optimal for pharmaceutical-specific tasks.² Clinical BioBERT, specialized for clinical notes, underperformed even further due to domain mismatch with label-specific terminology.²

The Solution: Specialized Models for Pharmaceutical Text

The answer is straightforward but powerful: train AI models specifically on actual drug label data. Researchers have developed PharmBERT, a new model released in 2024 and trained exclusively on over 138,000 FDA-approved drug labels from the DailyMed database.¹

How PharmBERT Works

PharmBERT starts with a general AI model and then undergoes additional training—called "domain pre-training “on actual pharmaceutical text. Consider teaching someone conversational English first, then providing specialized training in medical terminology and pharmaceutical regulations.¹

This targeted training produces measurable improvements across pharmaceutical tasks:

• Adverse Reaction Detection: PharmBERT correctly identifies warnings and adverse reactions 89.2% of the time, compared to 84%–85% for standard models—a meaningful improvement in safety signal detection.¹
• Drug-Drug Interactions: The model more accurately extracts information about how one medication interacts with another, a critical safety concern for healthcare providers. Recent systems achieve 88%–92% accuracy in extracting drug-drug interaction mentions from unstructured label text.³
• Pharmacokinetic Classification: When asked to automatically categorize how a drug is absorbed, distributed, metabolized, and excreted (ADME sections), PharmBERT achieves F1-scores exceeding 0.915, compared to 0.903 for BioBERT—improving regulatory assessment efficiency.¹

The most impressive performance comes when there's limited training data. When a pharmaceutical company works with rare disease therapies for which only a handful of labeled examples exist, PharmBERT outperforms standard models by over 40%.¹ This advantage is transformative for regulatory teams managing orphan medications or newly approved drugs.

Real-World Application: Accelerating FDA Product-Specific Guidance

The FDA's Product-Specific Guidance (PSG) program streamlines generic drug approvals by specifying exact bioequivalence requirements for each reference drug. Developing a PSG requires regulatory reviewers to manually extract pharmacokinetic information from the original drug label—a process consuming thousands of hours annually across FDA teams.

One pharmaceutical company deployed PharmBERT to automate this extraction process:

• Before: A reviewer manually extracted ADME sections, requiring 3–4 hours per drug label.
• After: PharmBERT pre-classified the sections with 91% accuracy, requiring only 30 minutes of human verification.
• Result: Across fifty annual drug evaluations, the company achieved a 60% reduction in submission cycle time, accelerating generic drug development timelines while maintaining safety oversight.

Handling Data Imbalance: When Some Drug Categories Are Rare

A second practical challenge emerges when working with actual pharmaceutical databases: class imbalance. Some drug categories appear frequently in FDA databases—common medications such as statins or antidepressants have hundreds of labeled examples. But rare disease therapies might have only a handful of examples.⁴

Traditional machine learning techniques address this by reweighting samples or by generating duplicate examples for rare categories. But these classical approaches have limitations.

Reweighting can overemphasize errors from the minority class, creating instability when labels contain noise. Duplication causes the model to memorize a limited set of examples rather than learn generalizable patterns.⁴

Recent research introduced an elegant solution called two-stage fine-tuning:⁴

Stage 1: Pre-Finetuning with Balanced Data

Instead of training directly on imbalanced real-world data, the model first trains on synthetically balanced data. Using generative AI (such as ChatGPT), researchers created augmented samples for minority classes while maintaining semantic consistency with originals.

Cosine similarity analysis indicates that augmented data retains 0.93 semantic similarity to the originals.⁴ This stage provides the model with an initial, unbiased representation in which all classes are represented equally, akin to teaching human experts the full landscape before specialization.

Stage 2: Standard Fine-Tuning

Only after this preparation does the model train on the actual, imbalanced dataset. This two-stage approach allows the model first to understand all classes equally, then adapt to real-world class distributions.⁴

The results are substantial. On ADME classification with naturally imbalanced pharmaceutical data, two-stage fine-tuning improved overall performance from 90.0% to 91.3%—a 1.4% absolute improvement that translates to 12%–18% relative improvement on minority classes (Huang et al., 2024).

More importantly, minority classes saw disproportionate improvement—the performance boost on underrepresented drug categories exceeded that of majority classes by a significant margin.

A contract research organization (CRO) that applied two-stage fine-tuning to adverse drug reaction classification improved detection of rare, serious adverse events by 14%, enabling earlier safety signals without increasing the false-positive rate for common events.⁵

Reducing Computational Complexity: Making Deployment Practical

A third critical challenge is computational cost. BERT-base contains 110 million parameters; fine-tuning all of them requires significant computing resources, extended training times, and substantial energy consumption. For pharmaceutical companies operating in global regulatory systems, these demands become substantial operational constraints.

Fine-tuning on a single V100 GPU for typical pharmaceutical tasks consumes 50–100 GPU-hours per task, multiplied across dozens of regulatory submissions annually.^1,6

Research identified a counterintuitive solution. You do not need to update all parameters. The LayerNorm component—a specific mathematical operation that normalizes network activations—undergoes the most significant changes during fine-tuning.

Fisher information analysis across standard benchmark tasks shows that LayerNorm parameters concentrate significantly more gradient information than attention heads, feed-forward networks, or embedding layers.⁶

By selectively fine-tuning only LayerNorm rather than the entire model, researchers achieved 95%–99% of the performance of full fine-tuning with only 20% of the parameters.⁶

This finding has immediate practical applications:

• Faster Deployment: Fine-tuning becomes feasible on standard hardware without specialized computing clusters.
• Reduced Training Time: Jobs are completed in hours rather than days.
• Environmental Impact: Dramatically lower carbon footprint—a 45%–55% reduction in GPU hours and 40%–50% reduced memory footprint.⁶
• Cost Efficiency: Eliminates expensive computing cluster requirements.

A pharmaceutical company implemented LayerNorm-only fine-tuning for its regulatory intelligence system, reducing per-model fine-tuning time from 4.2 hours to 1.9 hours on standard GPUs, thereby enabling rapid prototyping of task-specific models across multiple label analysis scenarios.⁶

Further refinement identified that fine-tuning only 25%–30% of LayerNorm parameters yields 92%–96% of full performance, further reducing computational burden.⁶

Strategic Implementation: A Four-Phase Roadmap

Phase 1: Foundation (Months 1-3)

Establish baseline capabilities by:

• Conducting an audit of your labeling documents to identify manual bottlenecks.
• Selecting your model platform (PharmBERT or custom fine-tuning approaches).
• Defining validation protocols aligned with FDA guidance.
• Assembling a cross-functional team of regulatory, informatics, and medical professionals.

Phase 2: Pilot Deployment (Months 4-6)

Validate capabilities on lower-risk tasks:

• Start with ADME classification or cross-label consistency checking.
• Fine-tune the selected model on one hundred–two hundred manually annotated label sections.
• Implement SHAP (Shapley Additive Explanations) analysis so humans understand how the AI reaches conclusions.⁷
• Establish workflows where AI recommendations receive human review.

Phase 3: Operational Integration (Months 7-12)

Scale successful pilots into regulatory workflows:

• Expand to complete label analysis (indication extraction, adverse reaction identification).
• Implement continuous retraining as newly approved labels enter your database.
• Develop company-specific pharmaceutical terminology reference databases.
• Train regulatory staff on AI-assisted workflows

Phase 4: Strategic Optimization (Year 2 and beyond)

Maximize efficiency and competitive advantages:

• Deploy two-stage fine-tuning for class-imbalanced tasks.
• Implement LayerNorm-only fine-tuning for computational efficiency.
• Explore advanced capabilities like analyzing both text and figures within labels.
• Monitor FDA and EMA regulatory guidance updates and adjust protocols accordingly.

Industry Adoption (2025)

The pharmaceutical industry is actively adopting these approaches. Several trends indicate the direction:

• Regulatory Acceptance: FDA guidance finalized in March 2025 increasingly acknowledges AI-assisted analysis of drug labeling for regulatory submissions, creating a clear pathway for integration.⁸ The guidance requires the definition of clear context, model validation, documentation of the architecture and limitations, audit trails, and human expert sign-off on final submissions.⁸
• Commercial Platforms: Major pharmaceutical companies have deployed internal AI systems for adverse event monitoring and drug interaction detection. Commercial platforms from companies such as IQVIA (Vigilance Detect system), Exscientia, and BenevolentAI now incorporate AI-based label analysis.⁹ IQVIA's system integrates AI-powered analysis with social media monitoring and scans more than eight million digital health records monthly, reducing case processing time from days to hours.⁹
• Data Standardization: FDA initiatives to standardize drug label data structures enable more sophisticated AI analysis, creating network effects as more labels enter standardized formats.

Generative AI Integration: ChatGPT-based data augmentation for minority class representation is now standard practice in pharmaceutical AI development.⁴

Practical Applications Across Pharmaceutical Operations

Pharmacovigilance Signal Detection

When a potential safety signal emerges—an unusually high number of heart problems reported with a cardiac medication —the safety team must quickly assess whether this represents a genuine concern or coincidental reporting. BioBERT-based systems automatically extract drug indications from Structured Product Labels with precision/recall of 0.918/0.908, enabling rapid cross-reference with reported adverse events.²

When a disproportionate signal is detected, the system immediately retrieves approved indications to determine whether the reported adverse event constitutes off-label use. A multinational pharmaceutical company deployed an AI system to monitor published literature across fifteen languages for emerging safety signals.

The system achieved 82% accuracy in distinguishing actual case reports from incidental mentions. Within the first year, the system flagged three emerging signals 2–3 months earlier than a traditional human-driven literature review, enabling regulatory submission within compressed timelines.

Cross-Label Harmonization for Global Submissions

When a drug approved in the United States requires approval in Europe, Japan, and Canada, each jurisdiction has slightly different label requirements. When regulatory updates occur, all jurisdictional labels must reflect on these changes consistently.

AI systems identify exactly which label sections require corresponding updates. A company implementing cross-jurisdictional label harmonization reported reducing harmonization cycle time from 6 weeks to 2 weeks by leveraging AI-powered difference detection.¹⁰

Adverse Reaction Mining at Scale

The Uppsala Monitoring Centre (WHO's global pharmacovigilance hub) processes millions of case safety reports annually. BioBERT-based systems automatically extract adverse reactions and safety warnings with 91.8% precision and 90.8% recall, enabling rapid cross-reference with reported cases.²

A multinational pharmaceutical company implementing this achieved 82% accuracy in distinguishing genuine case reports from incidental mentions, allowing human safety specialists to focus on genuinely relevant cases.

Drug-Drug Interaction Detection

A major pharmaceutical company deployed a BioBERT-based interaction-detection system for its pharmacy-benefit manager integration, enabling real-time alerts when patients are prescribed medications that interact. Within six months, the system identified 3.2% of prescriptions containing potential interactions, capturing 68% of interactions that human pharmacists would have flagged.³

Regulatory Considerations

As of December 2025, health authorities have not explicitly approved AI-generated sections of drug labels for regulatory submissions. However, the FDA's finalized guidance provides a clear pathway for responsible AI integration.⁸

Required Elements

Companies must clearly articulate their use of AI (e.g., "We use AI to generate first-pass label drafts from clinical data, requiring human expert review and validation"). Organizations must demonstrate that model outputs align with the training data, maintain audit trails documenting how AI reached specific conclusions, and establish procedures under which AI generates recommendations, but qualified experts make final decisions.⁸

Practical Implementation

Leading companies employ a hybrid approach in which AI generates first drafts and identifies inconsistencies while humans review for medical accuracy and regulatory appropriateness. One company implementing this approach reported that AI-assisted label drafting reduced writing time by 35% while requiring only 2–3 additional hours of expert review per submission—a net 40–50% efficiency gain when accounting for reduced rework cycles.¹⁰

Key Implementation Challenges and Mitigation Strategies

Challenge 1: Model Interpretability

AI systems operate as "black boxes” that even developers cannot fully explain why a model assigns specific classifications. Organizations address this through SHAP analysis, which decomposes model predictions into individual token contributions, and attention visualization to inspect which label sections the model weighted most heavily.⁷

A regulatory team using SHAP analysis found that their AI inadvertently assigned greater weight to descriptions of adverse event frequency ("rare") than to the events themselves. By reweighting training data, they corrected this bias.⁷

Challenge 2: Temporal Domain Shift

FDA label language evolves. Models trained on 2020–2022 labels may misinterpret contemporary documents using updated phrasing conventions.

Organizations mitigate this through continuous retraining cycles (quarterly or semi-annually), monitoring prediction confidence scores for out-of-distribution samples and maintaining human review thresholds for low confidence predictions.

Challenge 3: Bias and Underrepresented Drug Classes

Standard drug classes have hundreds of labeled examples; rare disease therapies may have only a few. Organizations deploy two-stage fine-tuning with synthetic augmentation for minority classes, use class-balanced loss functions during training, and establish separate model variants for ultra-rare drug classes.⁴

Limitations and Future Directions

These AI systems excel at extraction and pattern recognition, identifying adverse reactions, drug interactions, and dosing variations. However, complex pharmacological reasoning (predicting novel interactions in unprecedented drug combinations) remains primarily the province of experts. AI augments expert judgment rather than replacing it.

Emerging developments likely to shape the next 2–3 years include multimodal models that incorporate chemical structures and clinical trial data alongside text; causal reasoning that moves beyond pattern matching toward mechanistic understanding; real-time learning systems that continuously adapt as safety signals emerge; and direct FDA integration, embedding AI tools within regulatory review workflows.

Conclusion

Specialized AI language models trained in pharmaceutical data represent a fundamental capability upgrade for the industry. PharmBERT and related approaches collectively address operational challenges that regulatory, safety, and clinical teams face daily: faster information extraction, improved handling of rare drug categories, and efficient deployment without requiring expensive infrastructure.^1,4,6

For pharmaceutical professionals managing drug safety, regulatory compliance, or label analysis, the strategic imperative is clear: these tools are no longer experimental. They are operational systems delivering measurable efficiency gains—40%–65% time reductions in specific workflows, earlier detection of safety signals, and improved consistency across global regulatory submissions.

The models are publicly available, methodologies are documented, and competitive advantages accrue to early adopters. Organizations that master these capabilities will process regulatory information faster, detect safety patterns earlier, and maintain a competitive advantage through the coming decade of pharmaceutical development.

About the Authors

Partha Anbil is at the intersection of the Life Sciences industry and Management Consulting. He is currently SVP, Life Sciences, at Coforge Limited, a $1.7B multinational digital solutions and technology consulting services company. He held senior leadership roles at WNS, IBM, Booz & Company, Symphony, IQVIA, KPMG Consulting, and PWC. Mr. Anbil has consulted with and counseled Health and Life Sciences clients on structuring solutions to address strategic, operational, and organizational challenges. He was a member of the IBM Industry Academy, a highly selective group of professionals inducted by invitation only, the highest honor at IBM. He is a healthcare expert member of the World Economic Forum (WEF). He is also a Life Sciences industry advisor at MIT, his alma mater.

Partha Khot is the Life Sciences Practice Lead at Coforge, a $1.7B multinational digital solutions and technology consulting services company focused on driving innovation at the intersection of domain and technology. He held leadership roles at Triomics, Abbott, and Citiustech, driving healthcare innovation & consulting across the US, Europe, and India. Partha is responsible for developing next-generation Life Sciences Solutions at Coforge, built on Industry Platforms and differentiated through AI/Automation accelerators.

Prashant Deshpande is an internationally experienced IT executive with extensive business leadership, service design & delivery, and operations skills across the NA, LATAM, and Europe regions. He is currently VP, Life Sciences, at Coforge Limited. He brings 25+ years of solid IT services experience in the digital space and has held senior leadership roles at large-scale IT companies such as Cognizant, Capgemini, L&T Mindtree, and CMC, where he built service lines and competency units, managed P&L, and led large-scale, complex delivery and transformation programs across multiple domains, including HCLS. He also led initiatives for AI-based tools/ accelerator / QAE platform for LS to take it to market. In the past, Prashant spearheaded the QEA and Vertical SBU delivery of digital validation/compliance solutions that enhance customer satisfaction and operational efficiency & led the shaping of the industry's perspective on the pivotal role of regulatory and quality.

References

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