Regulatory Affairs Software for Medical Devices

Manufacturers of medical devices and in vitro diagnostic (IVD) devices must meet various regulatory obligations throughout the entire product lifecycle of their products. These obligations can vary depending on the product type and market. With a portfolio comprising multiple product families and target markets, the workload can quickly multiply. Added to this is the large volume of data that must be kept up to date, such as certificates, UDI records, standards status, complaint information, supplier documentation, and much more. Regulatory affairs software for medical devices promises to alleviate this burden.

This article provides an overview of compliance software for regulatory affairs in the medical device industry. It describes the categories of software solutions and online services available, what they can do, and where their limitations lie. It also addresses the use of AI in such tools and the potential implications of the AI Regulation for the functionality of these tools in the medical device sector.

Which regulatory tasks can be supported digitally?

Regulatory tasks can be effectively supported digitally, particularly when they involve similar data sets, follow recurring processes, or require structured documentation. These include, in particular, the control of documents and records; the traceability of requirements - from risks and risk control measures to verification and validation evidence; the management of product and approval/registration master data; the tracking of deadlines and tasks; and the analysis of post-market data.

The benefits of digital tools are particularly evident where completeness, consistency, and traceability must be verifiable. Examples include technical documentation, UDI data, certificates, standards status, complaint information, supplier documentation, CAPA actions, training records, and audit trails. Software can centrally manage this information, control versions, document releases, and highlight relationships between data sets.

Issues that require technical and regulatory interpretation are significantly more difficult to address. The intended purpose, classification according to Annex VIII of the MDR, the review of whether a change is substantial, and the benefit-risk review are decisions made by the manufacturer. Software can, at most, prepare, structure, and document such decisions. Generative AI can provide drafts, checklists, consistency checks, and counterarguments in this context. The technical evaluation, justification, and release always remain the responsibility of the manufacturer.

It should also be noted that software used in the quality management system (QMS) must be reviewed with regard to its regulatory relevance. ISO 13485:2016 requires, in section 4.1.6, the validation of such applications for their intended use. The scope of this validation should be based on the risk associated with the respective software function.

Compliance software: An overview of tools and services

The market for regulatory affairs and compliance software for medical devices is broad and cannot be clearly delineated. Many providers cover multiple functions, such as eQMS, design control, risk management, technical documentation, or regulatory master data. For orientation, tools and services can initially be grouped according to their purpose within the regulatory process.

Quality and development processes

These include tools that support quality management and product development. Among them are electronic quality management systems (eQMS), systems for design control and application lifecycle management (ALM), risk management tools, and solutions for cybersecurity and software bills of materials (SBOM). These systems help manage requirements, risks, risk control measures, verification and validation evidence, CAPA activities, changes, training, and audit trails in a traceable manner. They are particularly important where development decisions, quality processes, and evidence must be linked across the entire product lifecycle.

Regulatory operations

Tools in this group support regulatory operations. These include Regulatory Information Management (RIM) systems, tools for registration and labeling, solutions for UDI and EUDAMED data, and services for regulatory intelligence and standards management. Their focus is on the structured management and tracking of regulatory information, such as product, country, certificate, registration, and deadline data. The benefits increase with the number of product variants, target markets, and regulatory changes. Manufacturers with many products or international markets benefit particularly from a consistent regulatory data model and clearly defined responsibilities. Standardized information objects and data models that describe regulatory content across systems are also gaining in importance. They serve as a bridge between eQMS, ALM, RIM, technical documentation, and regulatory information for authorities and Notified Bodies.

Clinical evaluation, evidence, and post-market

This group includes software for clinical evaluation, performance evaluation, post-market surveillance (PMS), and vigilance. This encompasses literature review systems, EDC (Electronic Data Capture) systems for clinical investigations, performance studies, registries, PMCF (Post-Market Clinical Follow-up) and PMPF (Post-Market Performance Follow-up) surveys, as well as systems for complaint management, vigilance reports, FSCA (Field Safety Corrective Action) processes, trend analyses, and PSUR (Periodic Safety Update Report) submissions. These tools not only support data collection but also the traceable review, analysis, and integration of findings into risk files, clinical evaluation, performance evaluation, and technical documentation.

AI-specific infrastructure

Another category is emerging from AI-based medical devices and AI-supported business processes. This includes MLOps (Machine Learning Operations) and data management software, systems for versioning training, validation, and test datasets, model testing, drift monitoring, as well as governance tools for AI software components, risk evaluation, and proof of compliance. This category is not yet as well-established as typical eQMS or RIM solutions, but it is gaining importance due to the AI Act, MDR, IVDR, and the increasing use of generative AI in clinical and regulatory affairs.

Official portals and public databases play a special role in the context of compliance software. EUDAMED, EUR-Lex, the NANDO directory in the Single Market Compliance Space, DMIDS, and FDA portals such as GUDID (Global Unique Device Identification Database), MAUDE (Manufacturer and User Facility Device Experience Database), and eSTAR (electronic Submission Template and Resource) are not internal compliance tools, but rather regulatory reference, research, or reporting environments. Relevant data and processes must therefore be presented in a way that is interoperable with these portals.

The functions a manufacturer actually needs depend largely on the product type. A manufacturer of Class IIa hardware products with a single target market may be able to get by with eQMS, UDI, and label support. A manufacturer of a medical app may additionally need an ALM system and tools for SBOM and vulnerability monitoring. A manufacturer with twenty product families across fifteen markets will hardly be able to manage without a RIM system. The selection process should therefore not begin with vendor features, but rather with the manufacturer’s own processes, evidence risks, and regulatory considerations.

Key compliance tools and services for regulatory affairs in medical devices in detail

Electronic quality management system (eQMS)

An electronic quality management system (eQMS) digitally maps core quality management processes. These include document and record control, CAPA, change control, training records, supplier evaluation, audit management, and quality events. The primary benefits lie in version control, electronic release, audit trails, deadline tracking, and a traceable history of quality-related processes. For medical device manufacturers, an eQMS is particularly relevant when serving multiple products, sites, suppliers, or target markets. The software does not replace the quality management system itself but supports its controlled implementation and documentation.

Design control and application lifecycle management (ALM)

Design control and ALM systems support controlled product development. They manage requirements, specifications, architectural and design elements, verification and validation evidence, and the relationships between them. Traceability of requirements - from risks and risk control measures to test cases and test results - is particularly important. This makes it possible to demonstrate which requirements have been implemented, which risks have been controlled, and which evidence has been provided. For Medical Device Software (MDSW) - whether standalone or an integral part of a medical device - such systems also support the software lifecycle in accordance with IEC 62304. They are often integrated with development environments, test systems, ticket systems, or code repositories.

Risk management

Risk management tools support the creation and maintenance of the risk management file in accordance with ISO 14971. They map hazards, foreseeable sequences of events, hazardous situations, harm, risk evaluations, risk control measures, and evidence of effectiveness. A key aspect is the integration with requirements, design outputs, verification, validation, usability, post-market data, CAPA, and change control. Risk management is therefore often implemented as a module within an eQMS, design control, or ALM system. Standalone solutions are particularly useful when FMEA (Failure Mode and Effects Analysis), complex product structures, or technical risk analyses are the primary focus.

Regulatory information management (RIM)

Regulatory information management systems manage regulatory products, approval, registration, and country data. For example, they determine which product, in which variant, is legally marketable in which market, along with the applicable certificate, registration number, label status, and validity period. RIM systems also support submissions, renewal deadlines, change assessments, market access status, and portfolio analyses. Their value increases with the number of products, variants, and target markets. For smaller manufacturers with few products in a single market, a full-scale RIM system may be overkill. For manufacturers operating internationally, however, a properly structured data model can prove to be business critical.

Regulatory intelligence and standards management

Regulatory intelligence services monitor regulatory changes, guidelines, regulatory agency publications, and market access requirements across various jurisdictions. Standards management systems track edition statuses, withdrawals, replacement documents, and the harmonization status of applied standards. In practice, these two functions often work in tandem. Regulatory intelligence primarily helps identify relevant changes early on and review their impact on products, technical documentation, registrations, and processes. Standards management is particularly important for ensuring that evidence of compliance with general safety and performance requirements, the risk management file, technical documentation, and development records remain up to date. In-house solutions are often used for this purpose.

Clinical evaluation and evidence

Literature review systems support the systematic search and review of clinical literature. They assist with search strategies, duplicate removal, title and abstract screening, full-text review, data extraction, and documentation of inclusion and exclusion criteria. In doing so, they lay the foundation for a traceable clinical evaluation in accordance with Annex XIV of the MDR. EDC systems capture structured clinical data from clinical investigations, performance studies, registries, and PMCF and PMPF surveys. They provide electronic data capture forms, role- and permission-based access controls, plausibility checks, query management, audit trails, and exportable data sets.

Separate requirements apply to IVDs. The clinical evaluation is replaced by the Performance evaluation in accordance with Annex XIII of the IVDR, which comprises the three lines of evidence: scientific validity, analytical performance, and Clinical performance. Corresponding tools must be able to map this structure. In addition, for Class D products, batch testing and the involvement of EU reference laboratories, as well as the PSUR pursuant to Article 81 of the IVDR, are required.

Registration and labeling

Tools for registration and labeling manage UDI data, EUDAMED and GUDID data submissions, labels, IFUs, language versions, symbols, and packaging levels. They ensure that regulatory product data, labels, instructions for use, translations, and external database submissions remain consistent. This significantly reduces the effort required for maintenance, review, and release, especially when dealing with multiple markets, language versions, and product variants. Typical functions include UDI data management, data validation, barcode generation, label workflows, release processes, translation management, and IFU versioning. The structured provision of EUDAMED data is also becoming increasingly important, as the four EUDAMED modules - Actor Registration, UDI/Devices, Notified Bodies & Certificates, and Market Surveillance - will be mandatory as of May 28, 2026.

Post-market surveillance and vigilance

Post-market tools support post-market surveillance of products after they have been placed on the market. They record complaints, verify reporting obligations, and manage vigilance reports, FSCA, trends, CAPA references, and inputs for PSURs. An important aspect is feeding the findings back into Risk management, Clinical evaluation, Performance evaluation, instructions for use, technical documentation, and change control. Some of these services complement the analysis of public databases, such as MAUDE, to monitor comparable products and known incidents. Comprehensive post-market systems are therefore not merely case management systems; they integrate complaint data, safety assessments, trend analyses, and regulatory reporting requirements.

Cybersecurity and SBOM

For connected products and medical device software, a Software Bill of Materials (SBOM) - that is, an “inventory” of the software components used - and ongoing vulnerability monitoring are among the key means of demonstrating compliance with software and IT security requirements. The tools generate SBOM files in common formats, cross-reference components against vulnerability databases, and document the review of individual reports. The focus is not only on creating a list of components but also on ongoing vulnerability management. This includes the review of CVEs (Common Vulnerabilities and Exposures), the documentation of unaffected components, VEX information (Vulnerability Exploitability eXchange), patch decisions, risk acceptance, and feedback to PMS, risk management, and change management. Products covered by the MDR or IVDR are currently exempt from the Cyber Resilience Act. However, accompanying software, cloud components, and enterprise software that are not regulated as medical devices may be subject to the Act. This distinction should be documented during the tool selection process.

AI quality assurance and data management

This category is even newer than eQMS, RIM, or ALM and often originates from the MLOps environment. The tools version control training, validation, and test datasets, document data provenance, log training runs, manage model versions, perform model tests, and monitor performance or drift in the field. For AI-based medical devices, such functions are becoming significantly more important due to the AI Act, MDR, IVDR, and new normative requirements. MLOps tools alone do not provide proof of regulatory compliance. To do so, they must be linked to the risk management file, technical documentation, software lifecycle, quality management, PMS/PMCF/PMPF, and change management.

Benefits and limitations of software use

Regulatory affairs and compliance software can significantly reduce the effort required for recurring regulatory tasks. Among the most important benefits are:

consistency of regulatory data,
robust traceability,
reduced effort during audits and inspections, and
better scalability as the portfolio grows.

When dealing with multiple target markets, evidence, product data, documents, and Reviews can be reused more easily. A structured data model also prevents inconsistencies in product names, UDI data, certificates, labels, instructions for use, and technical documentation across different systems.

The software also improves transparency regarding open tasks, deadlines, and responsibilities. This applies, for example, to CAPA processes, changes, training, supplier evaluations, audit findings, complaints, vigilance assessments, and upcoming registration renewals. As a result, regulatory obligations become less dependent on specific individuals and easier to manage. Reports and supporting documentation can also be provided more quickly for management reviews, audits, and Notified Bodies.

However, these benefits are offset by the significant effort required to implement and operate the tools. Implementation can tie up a great deal of internal resources, as processes must be documented, roles and permissions defined, data migrated, templates customized, interfaces clarified, and users trained. The effort involved in migrating from legacy software, file servers, and spreadsheets can also be underestimated. Added to this is the requirement to validate the software for its intended use as part of the QMS in accordance with ISO 13485:2016, Section 4.1.6. The manufacturer must itself determine and demonstrate that the specific use, configuration, and process integration are suitable for the intended purpose. This also applies to relevant updates or changes, which must likewise be reviewed on a risk-based basis.

Another aspect concerns data quality. While software can perform testing to verify whether a field has been filled out, a document is present, or a process step has been completed, it cannot ensure that the content is technically correct, plausible, and sufficiently justified from a regulatory standpoint. For example, a formally complete traceability matrix in risk management may contain technically incorrect or insufficient links. Similarly, a fully completed risk data record may be methodologically weak, or a clinical evaluation may contain gaps in content despite error-free control of documents. This can lead to discrepancies during an audit.

Dependencies must also be considered. With software, the provider determines the update cycle, technical development, and, in some cases, the process logic. Switching providers later can be time-consuming if data, audit trails, releases, attachments, and links cannot be exported completely and in usable formats. Standalone solutions without usable interfaces can also lead to duplicate data maintenance across systems, such as when risk data is managed separately in the ALM system, complaint data in the eQMS, and registration data in the RIM system.

The use of software should therefore not be linked to the expectation that it will completely eliminate regulatory complexity. The benefits arise when the tool is tailored to a company’s own products, target markets, processes, and documentation requirements.

AI in compliance tools and services for regulatory affairs in medical devices

The use of AI in compliance tools and services for regulatory affairs in medical devices now ranges from simple assistance functions to specialized analyses in literature research, complaint management, technical documentation, regulatory intelligence, traceability, cybersecurity, and AI quality assurance. The practical benefit lies primarily in the ability to search large volumes of data more quickly, structure content, generate summaries, and highlight anomalies.

Currently, assistive functions are particularly widespread. These include, for example, the prioritization and classification of literature hits, AI-supported assistance with complaint logging, drafts for technical documentation content, summaries and comparisons of regulatory documents, translation suggestions for regulatory content, and indications of gaps in traceability links.

Generative AI is particularly helpful when information from multiple sources needs to be consolidated or structured. For example, it can prepare drafts for classification justifications, change assessments, clinical summaries, CAPA, or benefit-risk arguments. AI can also be used as a critical reviewer, for example, to identify inconsistencies between the intended purpose, risk profile, clinical evaluation, instructions for use, and marketing claims. However, the actual regulatory assessment remains the responsibility of the manufacturer.

More highly automated AI functions seem suitable in cases where the result is structured and easily verifiable. Examples include mapping vulnerability reports to SBOM components, checking for duplicates in reference lists, pre-structuring defect data, or identifying missing links between requirements, risks, and tests. Here, too, the (AI) software can provide guidance and prepare work steps, but it must not replace technical decisions without verification.

From a regulatory perspective, responsibility for content, accuracy, and completeness remains with the manufacturer. AI-generated or AI-assisted results should therefore be reviewed and approved by qualified personnel and, where relevant, documented as quality records. Here, too, the validation requirement under ISO 13485:2016 applies to AI-supported functions when they are used as part of the QMS. In addition, data protection, confidentiality, and data usage must be clarified, particularly whether inputs, attachments, or outputs are used for model training or vendor purposes.

Impact of the AI Act

For AI-based medical devices and IVDs, the AI Act adds another layer of regulation alongside the MDR and IVDR. AI-based medical devices and IVDs fall under the definition of “high-risk” pursuant to Article 6(1) of the AI Act if the AI system itself is a medical device/IVD or a safety component of such a product and the product is subject to a third-party conformity assessment under the MDR or IVDR. The Digital Omnibus Regulation (EU) 2026/1744 has postponed the effective date for high-risk AI under Article 6(1) in conjunction with Annex I to August 2, 2028. This should be distinguished from the AI functions described in the previous section in compliance software for regulatory affairs. As a general rule, these should not be classified as high-risk AI under Article 6(1).

High-risk AI medical devices impose new requirements on compliance tools for regulatory affairs in the medical device sector. In addition to traditional eQMS, ALM, risk management, and PMS systems, software is needed that enables the control of data sets, models, tests, performance monitoring, and changes throughout the life cycle of the product. The requirements of the AI Act pertain to risk management, data quality, technical documentation, logging, transparency, human oversight, accuracy, robustness, and cybersecurity.

Data quality is a key issue, meaning that training, validation, and test datasets must be managed in a traceable manner. This includes origin, purpose, representativeness, completeness, quality, potential biases, preprocessing, annotations, versioning, and access control. Traditional control of documents is only of limited use here because datasets are not static documents. Manufacturers therefore need tools that can version data sets, document data flows, and link data sets to model versions, tests, and regulatory evidence. The IEC PAS 63621:2026 (Publicly Available Specification) addresses this data management for AI-enabled medical devices.

Testing and verification procedures are also changing. For deterministic software, the focus is on whether defined inputs lead to defined outputs. For AI and ML models, validation is more statistically oriented, that is, software tools must comprehensively document performance metrics, acceptance criteria, test data sets, subgroup analyses, regression tests, robustness tests, and drift monitoring. This development is reflected in standardization, for example in the draft standard IEC 63450 for the testing of AI/ML-capable medical devices. The standards currently being developed by CEN-CENELEC JTC 21 - which are to be harmonized with the AI Act - also play a role in this context.

Overall, new interfaces are emerging between previously separate tool groups. MLOps tools can support dataset versioning, model training, model testing, and monitoring. ALM systems manage requirements, architecture, software changes, and verification. eQMS systems handle releases, change management, CAPA, training, and records. Risk management and PMS systems provide safety and field data. To provide robust evidence of compliance, this information must be consolidated. Since standards, their design, and vendor capabilities in this area are still evolving, manufacturers should ensure their tools remain adaptable and continuously monitor further design and development.

Approach to tool selection

From the manufacturer’s perspective, it is essential to determine which software can map the regulatory-relevant processes and support them in a way that can be validated. The following approach can help with the selection process:

Derive requirements from processes: First, define the process; then search for the tool. A software provider’s process logic should not be adopted without scrutiny.
Prioritize bottlenecks: Start by digitizing the function associated with the greatest manual effort or the highest risk of non-compliance.
Separate mandatory and optional requirements: A short, weighted list of requirements - for example, with 20-30 criteria - helps make decisions more objective.
Limit the shortlist to three providers: Apply exclusion criteria first, such as market coverage, location of hosting/data storage, export capability, validation documentation, multi-tenant capability, and exit scenario.
Conduct a pilot with real data: Run a real, complete process from start to finish using your own product data and users.
Jointly decide on the implementation and rollout plan: Migration, Validation, training, and decommissioning of the legacy software system should be part of a single Decision.

Conclusion

Compliance tools for regulatory affairs in the medical device industry can significantly improve the organization and scalability of documentation, data, and processes. However, they do not replace expert reviews or the manufacturer’s responsibility.

AI can further accelerate processes, for example through research, summaries, draft documentation, or traceability notes. However, its results must also be reviewed, validated, and approved by subject matter experts.

What matters most, therefore, is not the widest range of features, but whether a tool makes regulatory information reliable, traceable, and manageable throughout the product lifecycle - and whether it fits a Manufacturer’s processes.

Would you like to assess your tools or ensure an upcoming selection is sound? Schedule a no-obligation initial consultation.

Compliance Software for Regulatory Affairs in Medical Devices: An Overview of Tools and Services

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