AI in Healthcare and Biotech: Regulatory Landscape

Artificial intelligence systems operating within the European healthcare and biotechnology sectors exist at a complex intersection of product safety legislation, data protection mandates, and sector-specific clinical regulations. Unlike general-purpose AI applications, systems deployed in this domain are rarely governed by a single legal instrument. Instead, they are subject to a layered compliance environment where the AI Act converges with the Medical Device Regulation (MDR), the In Vitro Diagnostic Regulation (IVDR), and the European Medicines Agency (EMA) guidelines on computerised systems and big data. For professionals designing, validating, or procuring these technologies, understanding the interplay between these frameworks is not merely a legal exercise; it is a prerequisite for market access and patient safety.

The regulatory classification of an AI system in healthcare is determined primarily by its intended purpose and the risk it poses to human health. A software algorithm that monitors a patient’s vital signs for informational purposes may fall under a completely different regulatory regime than an algorithm that autonomously diagnoses a pathology or recommends a specific therapeutic intervention. The European regulatory architecture does not treat “AI” as a monolithic category; rather, it assesses the function of the device or medicine to which the AI is applied. This necessitates a granular analysis of the system’s role: is it a medical device, an accessory to a medical device, or a tool used in the development of a medicinal product?

The Convergence of the AI Act and Medical Device Legislation

The introduction of the Artificial Intelligence Act (AI Act) marks a significant shift in how high-risk technologies are regulated. While the MDR and IVDR have long established safety and performance requirements for medical technologies, the AI Act introduces horizontal rules applicable to all sectors, including health. Crucially, the AI Act designates most AI systems intended to be used as safety components of medical devices, or as medical devices themselves, as high-risk AI systems (Article 6(2) and Annex III). This creates a dual compliance burden: a medical device must satisfy the essential requirements of the MDR/IVDR, and simultaneously, the provider of the AI system must meet the obligations set out in the AI Act.

This dual regime implies that conformity assessments for AI-driven medical devices will increasingly involve the evaluation of algorithmic transparency, data governance, and robustness. Notified Bodies, which are already responsible for assessing compliance with the MDR and IVDR, are expected to integrate the evaluation of AI-specific risks into their procedures. However, the AI Act also introduces the possibility of third-party conformity assessment for high-risk AI systems by notified bodies designated specifically for AI. In practice, this may lead to a scenario where a medical device manufacturer requires validation from a Notified Body familiar with medical device risk management (ISO 14971) and potentially a separate assessment regarding the AI system’s lifecycle management and data quality, depending on the specific technical specifications required.

Scope and Applicability: When Does Which Law Apply?

It is vital to distinguish between AI systems that are “products” and AI systems that are “safety components.” Under the AI Act, an AI system that is itself a medical device (e.g., an AI-based imaging software used for diagnosis) is considered a high-risk AI system. Similarly, an AI system that is a safety component of a medical device (e.g., an algorithm controlling the dosage delivery of an infusion pump) falls under the high-risk category. The obligations for providers of such systems include establishing a risk management system, ensuring data governance quality, drawing up technical documentation, and enabling human oversight.

Conversely, AI systems used solely for research and development purposes, or those intended to improve the overall workflow of a hospital without directly influencing patient diagnosis or treatment, may fall outside the strict scope of the MDR/IVDR. However, they remain subject to the General Data Protection Regulation (GDPR) and potentially the AI Act if they are classified as high-risk in other contexts (e.g., biometric identification). The regulatory perimeter is therefore defined by the intended medical purpose and the level of impact on the patient.

Medical Device Regulation (MDR) and AI: The Software as a Medical Device (SaMD) Paradigm

The MDR (Regulation (EU) 2017/745) explicitly covers Software as a Medical Device (SaMD). Many AI systems, particularly those based on machine learning, fall into this category. The classification rules in Annex VIII of the MDR are critical here. They are based on the severity of the disease and the device’s purpose. For instance, an AI system intended to monitor vital physiological processes for immediate decision-making in a critical care setting is likely to be Class IIa or IIb, whereas a system providing information on physiological states for general wellness might be Class I.

However, the complexity of AI challenges traditional classification. An AI system might start as a Class I device but, through continuous learning (updates to its algorithm), effectively change its risk profile. The MDR requires manufacturers to plan for the post-market surveillance (PMS) and vigilance of their devices. For AI, this means having a robust system for monitoring the algorithm’s performance in the real world, detecting potential bias, and managing “drift” where the model’s performance degrades over time due to changes in the underlying data population.

General Safety and Performance Requirements (GSPR)

Annex I of the MDR outlines the General Safety and Performance Requirements. For AI systems, specific requirements regarding risk management, design and manufacturing, and information to be supplied with the device take on new dimensions. Manufacturers must demonstrate that the AI system minimizes the risks associated with the use of the device. This includes the risk of user error, which in the context of AI might involve understanding the limitations of the algorithm (e.g., the “black box” problem).

Furthermore, the information supplied by the manufacturer must include the intended purpose and any instructions for use. For an AI system, this implies that the manufacturer must provide sufficient information to allow the healthcare professional to understand the logic behind the AI’s output, or at least its limitations and accuracy metrics. The MDR does not explicitly mandate “explainability” in the way the AI Act does, but the requirement for transparency and the mitigation of risks associated with reasonably foreseeable misuse effectively pushes in the same direction.

IVDR and the Classification of AI in Diagnostics

The In Vitro Diagnostic Regulation (IVDR) (Regulation (EU) 2017/746) governs devices that are intended to examine specimens derived from the human body to provide information concerning physiological or pathological processes. AI is rapidly transforming this sector, particularly in pathology, radiology, and genetic sequencing. The IVDR classification system is risk-based, ranging from Class A (lowest risk) to Class D (highest risk).

Under the IVDR, software intended to process diagnostic data for the purpose of providing information on a patient’s condition is generally considered an IVD device if it meets the definition. The classification rules (Annex VIII) determine the level of scrutiny. For example, an AI system providing information on the detection of a life-threatening infectious disease (Class D) will require a Notified Body and rigorous clinical evidence. In contrast, an AI system providing information on pregnancy (Class A or B) faces a lighter regulatory burden.

One of the most significant challenges under the IVDR is the requirement for clinical performance data. AI algorithms require vast amounts of data for training and validation. Manufacturers must demonstrate that the algorithm performs as intended for the specific population it will be used on. This has led to intense debate regarding the use of retrospective data versus prospective clinical trials for algorithm validation. The regulatory expectation is shifting towards prospective validation for higher-risk devices to ensure that the algorithm generalizes well to real-world clinical settings.

The Impact of IVDR Transition and Notified Body Capacity

The transition from the In Vitro Diagnostic Directive (IVDD) to the IVDR has been fraught with difficulty, particularly regarding the availability of Notified Bodies. A significant percentage of IVDs previously self-certified under the IVDD now require Notified Body involvement under the IVDR. This bottleneck affects AI-driven diagnostics heavily, as many of these tools are classified as higher-risk (Class C or D) due to their impact on patient management decisions.

Manufacturers of AI-based IVDs must therefore engage with Notified Bodies early. The technical documentation for these devices is extensive, requiring detailed descriptions of the algorithm development process, data cleaning procedures, and validation results. The regulatory scrutiny of “black box” algorithms is particularly high in the IVD space, as the diagnostic output directly influences clinical pathways.

The Role of the EMA and Good Machine Learning Practice (GMLP)

While the MDR and IVDR regulate the device itself, the European Medicines Agency (EMA) regulates medicinal products. AI is increasingly used in the drug development lifecycle, from target discovery to clinical trial optimization and pharmacovigilance. The EMA has issued guidance on the use of AI in the regulatory lifecycle of medicines, emphasizing data integrity, validation, and transparency.

In 2023, the EMA and the FDA released a paper on Good Machine Learning Practice for Medical Device Development and Regulatory Decision-Making. While this is a US-EU collaboration, it reflects the harmonized thinking of European regulators. The principles include:

• Ensuring that the AI model is based on well-validated data.
• Managing the “human-AI interaction” effectively.
• Monitoring the model post-deployment to ensure safety.

For biotech companies using AI to analyze clinical trial data or to identify patient subpopulations, the EMA’s expectations regarding computerized system validation (CSV) apply. This means that the software tools used must be validated, and the algorithms used to generate data for regulatory submissions must be traceable and reproducible.

AI in Clinical Trials and Patient Selection

The use of AI to identify patients for clinical trials is a growing field. Here, the regulatory focus is on avoiding bias and ensuring that the selection process does not exclude protected groups. If an AI system is used as a medical device to screen patients for trial eligibility, it falls under the MDR/IVDR. If it is used purely as a tool by the sponsor to optimize recruitment, it falls under the EMA’s guidelines on clinical trials and data protection laws. The distinction is subtle but legally significant.

Data Governance: The Foundation of Compliance

Regardless of whether the framework is the AI Act, MDR, IVDR, or EMA guidelines, data governance is the common denominator. AI systems are data-hungry. In Europe, the processing of health data is strictly regulated by the GDPR and national laws implementing the Directive on the reuse of public sector information (Open Data Directive).

For AI systems to be compliant, the data used for training, validation, and testing must be:

1. Relevant and Representative: The data must reflect the population on which the AI will be deployed to avoid bias.
2. High Quality: The data must be accurate, complete, and free from errors that could mislead the algorithm.
3. Lawfully Processed: This is the most complex hurdle. Using patient data for training AI models often requires consent or a specific legal basis under GDPR. Furthermore, the secondary use of health data for research and innovation is being harmonized under the upcoming European Health Data Space (EHDS) regulation.

Bias and Fairness as Regulatory Risks

Regulators are increasingly viewing algorithmic bias not just as an ethical issue, but as a safety issue. If an AI system performs less accurately on a specific demographic group due to unrepresentative training data, it constitutes a failure of safety and performance requirements under the MDR/IVDR. The AI Act reinforces this by requiring data governance measures to prevent discriminatory outcomes. Manufacturers must therefore document the provenance of their data, the steps taken to mitigate bias, and the results of testing across different subgroups.

Practical Implementation: The Path to Conformity

For a company bringing an AI-based medical device or diagnostic tool to the European market, the practical path to compliance involves a multi-stage approach.

1. Early Regulatory Strategy

Before a single line of code is written for a production system, the regulatory pathway must be mapped. This involves determining the classification under MDR/IVDR and assessing if the system qualifies as high-risk under the AI Act. This classification dictates the conformity assessment route. If a Notified Body is required, the manufacturer must engage with them during the design phase, not just at the end.

2. Quality Management System (QMS) Integration

ISO 13485 is the gold standard for medical device QMS. Manufacturers of AI systems must integrate AI-specific processes into their QMS. This includes:

• Change Management: How are updates to the algorithm managed? (This is critical for “learning” systems).
• Software Development Lifecycle (SDLC): Adhering to standards like IEC 62304 for medical device software.
• Data Management: Procedures for data acquisition, labeling, and storage.

3. Technical Documentation and the “Technical File”

The technical file is the evidence of compliance. For AI, this document set is extensive. It must include:

• Algorithm Design and Development: A detailed description of the model architecture, the training methodology, and the hyperparameters.
• Performance Metrics: Sensitivity, specificity, AUC-ROC, and other relevant metrics, broken down by subgroups.
• Risk Management File: Identifying risks such as “hallucinations” (confabulations by the AI), data drift, and cybersecurity vulnerabilities.
• Instructions for Use (IFU): Clear guidance on the limitations of the AI and the requirement for human oversight.

4. Post-Market Surveillance (PMS) and Performance Follow-up

Compliance does not end at market entry. The AI Act requires a “Post-Market Monitoring System” specifically for high-risk AI systems. For medical devices, this overlaps with the PMS required by the MDR/IVDR. Manufacturers must actively collect data on the real-world performance of the AI. If the AI exhibits drift (changes in performance due to changing real-world data), the manufacturer must retrain the model and potentially re-evaluate its conformity.

Regulators are expected to ask for a “Real-World Performance Plan” (RWP) for high-risk AI medical devices. This plan details how the manufacturer will monitor the algorithm’s safety and effectiveness after it is deployed in diverse clinical settings.

National Implementations and Cross-Border Nuances

While the EU regulations provide a harmonized framework, national competent authorities (NCAs) play a significant role in enforcement and guidance. For example, the German Medizinproduktegesetz (MPG) and the French Code de la santé publique implement the MDR with specific national requirements regarding reporting and market surveillance.

Furthermore, the use of AI in healthcare touches upon national healthcare systems. Reimbursement is a key factor. In Germany, the Digital Health Applications (DiGA) fast-track process allows doctors to prescribe digital health apps reimbursed by statutory health insurance. For an AI-based app to qualify, it must provide medical benefits (positive healthcare effects). This is a separate assessment from the CE marking required by the MDR. Similarly, in France, the “Pérou” scheme allows for the early assessment and reimbursement of innovative health technologies. Navigating these national reimbursement pathways is as critical as regulatory compliance for commercial success.

Interoperability and Data Sharing

European healthcare systems are fragmented. AI systems often need to integrate with Electronic Health Records (EHR) across different Member States. The EU’s European Health Data Space (EHDS) regulation, currently in the legislative process, aims to create a framework for the exchange of health data. For AI developers, the EHDS will likely create a mechanism to access anonymized or pseudonymized data for training and validation, subject to strict governance. It will also impose interoperability requirements on AI systems to ensure they can communicate effectively with health IT infrastructure across the EU.

Cybersecurity and the AI Act

AI systems in healthcare are prime targets for cyberattacks. A compromised AI system could lead to misdiagnosis, incorrect treatment, or large-scale data breaches. The AI Act explicitly requires high-risk AI systems to be resilient against attempts by unauthorized third parties to alter their use or performance. This includes robustness against adversarial attacks (inputs designed to fool the AI).

Additionally, the Cyber Resilience Act (CRA), once adopted, will impose cybersecurity requirements on products with digital elements. Medical devices and high-risk AI systems will fall under the scope of the CRA, requiring manufacturers to ensure that vulnerabilities are managed throughout the product’s lifecycle. This adds another layer of security obligations on top of the MDR/IVDR requirements for device safety.

Generative AI in Healthcare

The rise of Large Language Models (LLMs) and Generative AI introduces specific regulatory challenges. If a generative AI model is used to draft medical reports or summarize patient interactions, it may be considered a high-risk AI system under the AI Act if it influences a medical decision. The “foundation model” providers (e.g., the companies building the base LLMs) have specific obligations regarding copyright and downstream transparency.

For healthcare providers using these tools, the regulatory burden shifts to “deployers.” Under the AI Act, deployers of high-risk AI systems must ensure human oversight, monitor the system, and manage input data. A hospital using an LLM to assist in triage must ensure that the system is used appropriately and that staff are trained to recognize its limitations. The EMA has also issued warnings about the use of AI in regulatory submissions, stressing that the ultimate responsibility for the accuracy of data lies with the applicant, not the AI tool.

Future Outlook: The European Health Data Space and Beyond

The regulatory landscape for AI in healthcare is stabilizing but remains dynamic. The full implementation of the AI Act will take place over the next few years, with the prohibitions on unacceptable risk AI applying first (February 2025), followed by the general applicability of the Act for high-risk systems (August 2026). However, the MDR and IV