During a June 2025 webinar on pragmatic AI applications in healthcare quality management and regulatory affairs, live polling of quality and regulatory professionals revealed that approximately 80% of respondents were actively implementing AI solutions or seriously considering their use in quality management and regulatory activities.

This finding underscores a fundamental shift in how the healthcare industry approaches quality assurance and global regulatory compliance.

The enthusiasm for AI-enabled quality and regulatory processes isn’t unfounded. As quality and regulatory professionals navigate increasingly complex global requirements that span different product types, varying country-specific regulations, and evolving clinical applications, a pragmatic approach to AI presents an opportunity to improve one or more of process quality, process effectiveness, resource use, and global compliance while maintaining rigorous standards for patient safety and product quality.

Understanding AI’s role in quality management and regulatory affairs 

AI in healthcare quality management and regulatory affairs isn’t a futuristic concept, but rather a fact. However, successful implementation requires understanding which AI approaches best address specific quality management and regulatory challenges. The key question for targeted, pragmatic solutions is: How can an appropriate AI solution be used in a way that focuses on patient safety, product quality, and commercial performance while complying with global regulations?

Different quality management and regulatory processes benefit from distinct AI approaches, such as:
• Machine learning (ML) for pattern recognition in data, such as adverse events and product quality issues
• Natural language processing for complaint analysis and regulatory document review
• Image recognition for visual inspection processes and verification of authentic or counterfeit products
• Generative AI for a range of documentation drafting and translation capabilities
• Decision-tree algorithms for regulatory pathway recommendations

This variety of available AI technologies means organizations must first clearly define their use cases and understand the surrounding context of working in a regulated environment before selecting appropriate solutions.

The critical role of data quality 

Data are the foundation for effective AI implementation, and it’s important to understand a fundamental principle: Bad data equal bad output. For AI to deliver meaningful results in quality management and regulatory affairs, organizations must ensure that data structure and format align with, and are optimized for, AI processing requirements. They also must maintain relevance and be properly curated and accessible. In addition, regulatory intelligence should be continuously updated to reflect changing global requirements.

The data challenge extends beyond simple collection. Healthcare organizations must harvest current regulatory data from their regulatory intelligence platforms, then intelligently curate this information to feed quality management and regulatory workflows. This includes maintaining up-to-date submission structures as countries use pharmaceutical electronic common technical document (eCTD) submission formats and a range of local-country formats and submission types and pathways for medical technology.

Practical AI applications in eQMS and regulatory information management (RIM) 

Several specific use cases demonstrate how AI transforms daily quality management and regulatory activities.

Global registration planning: AI-enabled systems can analyze product characteristics, risk classifications, and target markets to recommend optimal regulatory pathways. Rather than relying solely on tribal knowledge accumulated over years, regulatory teams can leverage AI to quickly determine timelines, fees, and submission requirements across multiple countries, and produce strategic plans that optimize market access timelines and product launch pathways.

Impact assessment: When regulations change, AI can rapidly assess which products, processes, or documentation require updates, automating what historically required significant manual effort. This allows companies to consciously understand the end-to-end effect of change, including how market registrations are affected, so that any approved change plan can have the appropriate level of assigned resources and budget allocation.

Complaint management: Natural language processing (NLP) and event recognition capabilities enable AI to categorize, prioritize, and route complaints more efficiently while identifying patterns that might indicate systemic quality issues or deviations from known safety profiles. The ability to use AI to improve the quality, volume, and timeliness of case intake has a direct effect on ensuring a sustained focus on product quality and patient safety.

Documentation and translation: Generative AI accelerates the creation of first drafts, such as instructions for use, operative techniques, summary of product characteristics, patient information leaflets, and regulatory submissions. For global organizations, AI-powered translation capabilities can significantly reduce time to market in multiple geographies and accelerate the performance of professional, human-in-the-loop reviewers who can revise these drafts to an approved status.

Regulatory intelligence: AI continuously monitors and consolidates regulatory changes across jurisdictions, ensuring that quality teams stay current with evolving requirements in their target markets. Monitoring global regulatory change and associated design inputs allows industry to stay alert to any actions that may be needed to accelerate new products to market or maintain market presence of existing solutions.

Navigating the global AI regulatory landscape 

As healthcare organizations implement AI in their QMS and RIM systems, they face a dual regulatory challenge: complying with traditional healthcare regulations while also navigating emerging AI-specific requirements and global data protection requirements.

There’s no one-size-fits-all solution. Multiple jurisdictions have introduced AI governance frameworks that affect how healthcare companies can deploy AI technologies. The European Union AI Act classifies AI systems by risk level and imposes corresponding requirements, while in the U.S., the FDA provides guidance on AI/ML-based medical devices and quality systems. In addition, various national frameworks have been developed to address AI governance, data privacy, and algorithmic transparency. As a result, quality and regulatory professionals must now develop expertise in AI regulations alongside their traditional healthcare compliance knowledge. This complexity reinforces why thoughtful, pragmatic AI implementation, and not wholesale adoption, makes strategic sense.

The methodology for successful AI implementation 

Deploying AI successfully mandates a structured approach similar to design control processes.

Define the use case: What specific problem needs solving? What outcomes define success?

Understand global regulatory constraints: Which regulations and standards apply to the company’s product ranges? What verification and validation requirements must be met, and how could the introduction of AI affect current company certifications?

Assess available solutions: What is the cost to deploy and maintain the proposed solution? Is AI the best approach, or could alternative solutions be more effective?

Evaluate data requirements: Do sufficient quality data exist to train and maintain the AI system? What data governance is in place to ensure the ongoing monitoring of AI solutions and how they are used by human-in-the-loop professionals?

Calculate total cost: Consider not just implementation costs but ongoing maintenance, governance, revalidation, and compliance requirements.

For small and medium-sized enterprises with limited budgets, focus becomes paramount. Picking that one area that could benefit from AI to drive the most significant value is key. Organizations should, therefore, green-light high-impact opportunities instead of attempting comprehensive AI deployment.

The patient safety imperative 

While it might be tempting to overreach on AI deployment, it’s vital that the organizational focus always remains on advancing patient safety through improved product quality. AI serves as an enabling technology, not an end goal.

When properly implemented, AI can enhance patient outcomes through three interconnected paths.

Product quality: AI helps identify potential quality issues earlier, enables more thorough analysis of postmarket data, and facilitates continuous improvement through better feedback loops between market experience and product design.

Regulatory compliance: By automating routine compliance activities and providing better regulatory intelligence, AI frees quality and regulatory professionals to focus on strategic market-access activities and data-driven, proactive quality improvement.

Commercial performance: More efficient quality and regulatory processes accelerate time-to-market, reduce compliance costs, and enable organizations to invest more resources in innovation, ultimately expanding patient access to new therapies and devices.

Building AI-enabled quality systems 

Modern eQMS and RIM platforms increasingly incorporate AI as core platform capabilities rather than bolt-on features, integrating multiple AI capabilities at the platform level. For example:
• AI prompts for user guidance
• NLP for document analysis
• Event recognition for automated categorization
• Summarization features for complex data
• Decision tree recommendations for process guidance
• Text and image ingestion capabilities

These foundational capabilities then support specific business-use cases across different quality management modules. As the user interface evolves, quality and regulatory professionals gain access to AI assistance contextually—where and when it adds value to their workflows.

The path forward: From assessment to implementation 

With many healthcare organizations exploring AI for quality management and regulatory solutions, the industry stands at an inflection point. The question is no longer whether AI will transform quality and regulatory activities, but how quickly and effectively organizations can move from assessment to practical implementation that yields tangible benefits.

Several factors will determine success:
• Data maturity remains the greatest limiting factor. Organizations must invest in data infrastructure and governance as a key driver to significant AI returns.
• Global regulatory clarity continues to evolve. Quality and regulatory professionals should stay informed about AI-specific regulations in their target markets and engage with industry groups shaping these frameworks.
• Change management will prove critical. AI implementation requires training quality and regulatory teams on new tools and workflows while maintaining validation throughout transitions.
• Measured expectations help avoid disillusion. Starting with focused, high-value use cases builds organizational confidence and demonstrates ROI before broader deployment.

Conclusion 

The fact that many healthcare organizations are implementing or assessing AI in quality management and regulatory affairs reflects both excitement about AI’s potential and recognition of QMS and RIM complexity. As global regulations multiply, product portfolios expand and patient safety expectations rise, AI offers quality and regulatory professionals powerful tools to meet these challenges.

However, success requires more than enthusiasm. It demands careful attention to data quality, thoughtful use case selection, proper regulatory compliance, a robust governance framework, and realistic expectations about implementation timelines and resource requirements.

For the healthcare industry, the goal remains constant: ensuring that safe, effective products reach patients efficiently. When implemented with the same rigor and care that quality professionals bring to all aspects of their work, AI represents a pragmatic tool to advance this mission.