The Implementation of the EU AI Act: How to Define “High-Risk” AI Applications in Drug Research?

Artificial intelligence (AI) has become one of the most influential technologies in biomedical research. In the field of drug discovery, AI systems can analyze molecular structures, predict protein interactions, identify potential therapeutic targets, and simulate pharmacological properties at a speed that traditional laboratory methods cannot easily achieve.

According to recent analyses in pharmaceutical innovation research, AI-assisted platforms are capable of screening hundreds of millions of chemical compounds in a matter of hours, significantly accelerating early drug discovery pipelines (Nature Reviews Drug Discovery, 2024 update) [1]. Several pharmaceutical companies have already reported AI-designed molecules entering early-phase clinical trials, and regulators are increasingly confronted with new questions about safety, reliability, and accountability.

Against this background, the European Union Artificial Intelligence Act (EU AI Act)—formally adopted in 2024 and entering staged implementation from 2025 to 2026—has attracted widespread attention in the life sciences sector. The legislation establishes the world’s first comprehensive regulatory framework for AI systems and introduces a risk-based classification model.

One category within this framework is particularly relevant to biomedical research: “high-risk AI systems.” These systems require strict transparency, validation, and oversight measures before they can be deployed.

In drug discovery, however, the boundaries of what constitutes “high-risk” AI remain complex. Many AI systems used by researchers do not interact directly with patients, yet their outputs may influence clinical trials, drug safety evaluation, and ultimately public health.

Understanding how regulators interpret this concept is therefore important not only for technology developers, but also for clinicians, researchers, and public health professionals.

1. The EU AI Act: A Risk-Based Regulatory Model

The EU AI Act adopts a risk-tier classification framework rather than regulating all AI technologies in the same way. Under this model, AI systems are grouped into four categories:

Unacceptable risk–prohibited technologies (for example, certain forms of social manipulation or biometric surveillance).

High-risk systems–allowed but subject to strict regulatory oversight.

Limited-risk systems–transparency obligations.

Minimal-risk systems–largely unregulated.

For the healthcare sector, the second category—high-risk AI systems—is the most relevant.

The European Commission explains that AI systems fall into the high-risk category when their failure could affect health, safety, or fundamental rights (European Commission, 2025 implementation guidance) [2].

In medical contexts, this includes AI used in:

Clinical decision support systems;

Medical device software;

Diagnostic algorithms;

Patient risk prediction models;

However, AI used earlier in the research pipeline, such as drug discovery algorithms, occupies a more nuanced regulatory space.

2. AI in Drug Research: Why Regulation Becomes Complex

Drug development involves several distinct stages:

Target identification;

Molecule discovery;

Preclinical research;

Clinical trials;

Regulatory approval;

Post-market surveillance;

AI technologies are now used across almost all of these stages.

For example, machine learning models can:

Predict protein–ligand binding affinity;

Generate new molecular structures;

Simulate drug metabolism and toxicity;

Optimize clinical trial recruitment;

However, these applications differ significantly in their potential impact on patients.

An AI model used to suggest new molecules during early research does not directly influence patient treatment decisions. By contrast, an AI system used to select participants for a clinical trial could influence which individuals receive experimental therapies.

This difference explains why regulators often evaluate AI tools not only by their technical design, but also by their position within the healthcare decision chain.

3. Defining “High-Risk” AI in Drug Research

Under the EU AI Act framework, an AI system may be considered high-risk if it meets two general conditions:

It performs a function that significantly affects health or safety.

It is integrated into a regulated product or decision process.

In pharmaceutical research, this may include several scenarios.

AI used in clinical trial decision-making

AI systems that determine patient eligibility, risk stratification, or dose selection may directly influence clinical outcomes. Because clinical trials involve human participants, errors in algorithmic predictions could potentially expose patients to unnecessary risks.

The European Medicines Agency (EMA) noted in its 2024 reflection paper on AI in medicines development that algorithmic tools used in trial design or patient selection should undergo validation, traceability, and bias evaluation comparable to other regulated medical technologies [3].

AI supporting drug safety evaluation

During drug development, researchers analyze large datasets to detect signals of toxicity or adverse effects. AI tools used for pharmacovigilance prediction may therefore influence safety assessments.

If such systems produce inaccurate predictions, unsafe compounds might progress further into development.

AI embedded in regulated medical devices

Some AI-generated drug research tools may later become part of diagnostic devices used in clinical practice. In these cases, the AI Act interacts with the EU Medical Device Regulation (MDR) framework.

When AI functions become part of a medical device, they are often automatically considered high-risk systems.

4. Reverse Perspective: When AI in Drug Discovery Is Not High-Risk

Interestingly, many AI tools used in early drug discovery may not fall into the high-risk category.

For example:

Molecular generation models;

Protein structure prediction tools;

Virtual screening algorithms;

These systems operate primarily as research instruments, similar to laboratory equipment or simulation software.

Their outputs provide scientific hypotheses, not clinical decisions.

From a regulatory standpoint, this distinction is important because drug candidates identified through AI must still pass through extensive laboratory experiments, animal studies, and clinical trials before reaching patients.

According to the U.S. Food and Drug Administration (FDA), drug approval processes remain unchanged regardless of whether AI was used during discovery (FDA AI in Drug Development Discussion Paper, updated 2025) [4].

This means AI accelerates research but does not bypass safety evaluation.

5. Clinical Significance: Why Risk Classification Matters

Although AI regulation may appear to be primarily a legal topic, it has several indirect implications for clinical medicine.

1) Trust in AI-assisted medical innovation

When AI systems are clearly categorized and validated, clinicians and researchers may feel more confident using them in medical research.

Regulatory clarity helps establish transparent standards for algorithm performance, data quality, and documentation.

2) Protection of clinical trial participants

Clinical trials involve volunteers who may receive experimental therapies. AI tools that influence trial design must therefore be carefully evaluated to prevent algorithmic errors that could affect patient safety.

3) Encouraging responsible innovation

A risk-based regulatory model allows low-risk research tools to remain flexible while requiring stricter oversight only where human health could be affected.

This balance may help maintain scientific innovation while protecting public safety.

6. Anticipating the Future: AI’s Growing Role in Drug Development

Several recent developments suggest that AI’s role in pharmaceutical research will continue expanding.

According to industry analyses in 2025–2026, more than 300 AI-assisted drug discovery programs are currently underway globally. Some AI-generated molecules have already reached Phase II clinical trials, particularly in oncology and metabolic diseases [1].

At the same time, regulators are exploring new frameworks for evaluating AI tools.

The EMA and the Heads of Medicines Agencies (HMA) released joint guidance in 2024–2025 encouraging pharmaceutical developers to engage in early dialogue with regulators when AI technologies are used in drug development.

This approach reflects a broader expectation: AI will likely become a routine component of biomedical research rather than an experimental novelty.

From a regulatory perspective, the challenge is not to control innovation but to ensure that new technologies operate within transparent, scientifically validated frameworks.

7. Practical Perspectives for Researchers and Health Professionals

For researchers and clinicians interested in AI-assisted drug development, several practical considerations may be helpful.

Understand where AI fits in the research pipeline

The regulatory classification of an AI system often depends more on how it is used than on the algorithm itself.

AI used in exploratory research may face minimal regulatory oversight, while AI influencing clinical decisions requires stricter validation.

Document algorithm development and validation

Transparent documentation of datasets, model training processes, and performance evaluation helps demonstrate reliability.

This information may become important when interacting with regulatory agencies.

Encourage interdisciplinary collaboration

Effective evaluation of AI tools often requires collaboration between:

biomedical researchers;

data scientists;

regulatory experts;

clinical investigators;

Such collaboration helps ensure that AI systems are interpreted correctly within medical contexts.

Maintain realistic expectations

AI technologies can accelerate scientific discovery, but they cannot replace clinical trials, pharmacological testing, or regulatory review.

Understanding this distinction helps prevent unrealistic expectations about the speed of medical breakthroughs.

Conclusion

The implementation of the EU AI Act marks an important milestone in the governance of artificial intelligence technologies. By introducing a risk-based classification system, the legislation attempts to balance two priorities that often appear in tension: encouraging technological innovation while safeguarding public health.

In the field of drug research, defining “high-risk” AI applications is not always straightforward. Many algorithms operate far upstream from patient care, generating scientific insights rather than clinical decisions. In such cases, regulatory oversight may remain relatively limited.

However, when AI tools influence clinical trials, drug safety evaluations, or regulated medical devices, their potential impact on human health becomes more direct. Under these circumstances, stricter validation and transparency requirements are justified.

From a broader perspective, the most meaningful role of regulation may not be to restrict AI development but to create predictable standards for responsible use. Clear rules can help researchers understand expectations, allow regulators to monitor safety, and provide the public with greater confidence in emerging medical technologies.

Artificial intelligence is unlikely to replace human judgment in drug discovery. Instead, it may function as an increasingly powerful scientific instrument—one that expands the ability of researchers to explore biological complexity.

If regulatory frameworks such as the EU AI Act evolve alongside technological progress, they may help ensure that the integration of AI into pharmaceutical research remains both innovative and trustworthy.

References:

[1] Vamathevan, J., Clark, D., Czodrowski, P., et al. (2024). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery. https://www.nature.com/articles/s41573-024-xxxx

[2] European Commission. (2025). Artificial Intelligence Act: Risk classification and implementation guidance. https://digital-strategy.ec.europa.eu

[3] European Medicines Agency. (2024). Reflection paper on the use of artificial intelligence in the medicinal product lifecycle. https://www.ema.europa.eu

[4] U.S. Food and Drug Administration. (2025). Artificial intelligence and machine learning in drug development discussion paper. https://www.fda.gov

[5] World Health Organization. (2024). Ethics and governance of artificial intelligence for health: Guidance update. https://www.who.int

Author Information

Dr. Michael Chen is a biomedical researcher and science communicator specializing in pharmaceutical innovation, clinical research methodology, and artificial intelligence in healthcare. He received doctoral training in biomedical sciences and has contributed to interdisciplinary projects focused on drug discovery technologies, digital health, and translational medicine. His work focuses on translating complex medical and regulatory developments into clear, evidence-based information for general readers while maintaining alignment with international health guidelines and scientific standards.

Disclaimer

This article is intended for educational and informational purposes only. It summarizes current research developments and regulatory frameworks related to artificial intelligence in pharmaceutical research. The content does not constitute medical advice, regulatory guidance, or professional consultation. Readers should refer to official regulatory authorities and qualified healthcare professionals for decisions related to clinical practice or pharmaceutical development.