Artificial Intelligence in Clinical Development: What FDA and EMA Expect in 2026

Artificial intelligence has become one of the most talked-about topics in healthcare. Hardly a week goes by without news about a new AI tool that promises to accelerate drug development, improve patient care, or transform the way companies work.

But beyond the headlines and excitement, something much more significant is happening in clinical research.

For years, AI was viewed mainly as a tool to improve efficiency. It could help organize information, summarize documents, or automate repetitive tasks. Today, the conversation is changing. Regulators are beginning to address a much bigger question:

The answer is becoming increasingly important for pharmaceutical companies, medical device developers, CROs, and regulators alike.

AI Is No Longer Just About Productivity

When people think about AI, they often imagine chatbots generating text or software helping write documents.

Those applications certainly exist, and many organizations are already using AI-assisted tools to support activities such as literature reviews, document preparation, and information management.

However, the most important development is that AI is starting to move closer to the science itself.

Instead of simply helping teams prepare submissions, AI is increasingly being used to analyze images, identify patterns in data, assist with diagnoses, and generate information that may contribute to regulatory decisions.

This shift changes the role of AI completely.

The discussion is no longer only about working faster. It is about determining whether AI-generated outputs are reliable enough to be used as part of the evidence supporting the development of new drugs and medical devices.

Regulators Are Paying Attention

Recognizing the growing role of AI in healthcare and clinical research, regulatory authorities have begun establishing frameworks for its use.

In January 2025, the U.S. Food and Drug Administration (FDA) published its first guidance specifically focused on artificial intelligence used to support regulatory decision-making for drugs and biological products.

What is interesting about this guidance is that it does not ask whether companies should use AI.

Instead, it focuses on how organizations can demonstrate that an AI system is reliable, properly validated, and suitable for its intended purpose.

Recent FDA and EMA publications indicate that the discussion has moved beyond whether AI will be used in healthcare and clinical development. The focus is increasingly on ensuring that AI systems are appropriately validated, transparent, fit for purpose, and capable of producing outputs that can be trusted within regulated environments.

A year later, the FDA and the European Medicines Agency (EMA) jointly published a set of principles for the responsible use of AI in drug development.

For companies operating internationally, this was an important signal. Two of the world's leading regulatory authorities were effectively saying the same thing: innovation is welcome, but it must be supported by transparency, validation, oversight, and accountability.

A Milestone That Changed the Conversation

One event in particular highlighted how far the field has progressed.

At first glance, this may seem like a very specific application. In reality, it represents something much larger.

Importantly, the AI system does not replace pathologists. Human experts remain responsible for the final interpretation.

But the qualification of AIM-NASH demonstrated that AI can move beyond administrative support and become part of the scientific process itself. That is a major milestone for the industry.

Why Governance Matters More Than Ever

One of the recurring themes in recent FDA and EMA publications is governance.

While public discussions often focus on technological breakthroughs, regulators are asking practical questions.

These questions may not sound exciting, but they are becoming increasingly important.

As AI becomes more integrated into clinical development, organizations will need to demonstrate not only what technology they are using, but also how it is managed, monitored, and controlled.

In many ways, the same principles that have always applied to clinical research - quality, documentation, transparency, and accountability - are now being applied to AI as well.

AI Is Already Influencing Medical Device Development

The growing impact of AI is not limited to pharmaceuticals.

Many modern medical devices now incorporate artificial intelligence or machine learning technologies. This trend is especially visible in fields such as radiology, pathology, cardiology, and ophthalmology.

In ophthalmology, AI-assisted systems are already being used to analyze retinal images, support disease detection, and assist with screening programs.

As these technologies continue to evolve, developers are increasingly expected to think beyond traditional device performance. Questions about algorithm validation, transparency, monitoring, and long-term reliability are becoming part of the development process from the earliest stages.

For many companies, these considerations are no longer optional. They are becoming an essential part of regulatory strategy.

Could AI Change the Way Clinical Trials Are Conducted?

Beyond submissions and medical devices, researchers are also exploring how AI could improve clinical trial design and execution.

Areas currently being investigated include:

• Patient identification
• Site selection
• Recruitment planning
• Endpoint assessment
• Data analysis
• Trial feasibility assessments

Many of these applications are still evolving, and regulators continue to evaluate how they should be validated and implemented.

Nevertheless, they offer a glimpse into the future of clinical development. The real opportunity is not simply to complete the same tasks more quickly. It is to generate better evidence, make more informed decisions, and potentially improve the efficiency of clinical research as a whole.

AI Is Also Changing Regulatory Operations

Artificial intelligence is influencing not only scientific evidence generation but also the operational side of regulatory affairs.

Across the industry, organizations are exploring AI-assisted approaches for:

• Literature reviews and regulatory intelligence
• Medical writing support
• Submission document preparation
• Protocol and informed consent reviews
• Safety signal detection
• Regulatory gap assessments
• Quality management activities

Importantly, current regulatory expectations emphasize that AI-assisted outputs should be reviewed by qualified professionals and that organizations remain accountable for the content submitted to health authorities.

Why This Matters for Emerging Biotech and MedTech Companies

For smaller biotechnology and medical device companies, artificial intelligence may offer opportunities to improve efficiency and access expertise that was previously resource-intensive.

However, regulators are increasingly focusing not only on whether AI is used, but on how it is governed. Organizations must be prepared to demonstrate:

• Appropriate validation
• Human oversight
• Data integrity
• Documentation of AI-assisted processes
• Ongoing monitoring of system performance

As regulatory expectations evolve, effective AI governance may become just as important as the technology itself.

What to Consider When Evaluating AI Capabilities in Clinical Development

As AI becomes increasingly integrated into the life sciences industry, organizations should understand how these technologies are being used and managed.

Several considerations are becoming particularly important.

Ultimately, the goal is not to use artificial intelligence wherever possible. The goal is to apply it thoughtfully, in ways that improve efficiency and support decision-making while maintaining the quality and scientific rigor required in clinical research.

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The aCROss Medical Perspective

At aCROss Medical, we see artificial intelligence as one of many technologies that can help support modern clinical development.

Like any tool, its value depends on how it is applied.

Recent guidance from the FDA and EMA makes it clear that successful use of AI requires more than advanced technology. It requires validation, transparency, oversight, and scientific judgment.

These principles have always been fundamental to clinical research.

Whether supporting regulatory activities, document preparation, quality review, or operational planning, we believe technology should strengthen expertise rather than replace it.

The organizations that will benefit most from AI are unlikely to be those that adopt it simply because it is new. They will be the ones that integrate it thoughtfully while maintaining the scientific, regulatory, and quality standards that successful development programs depend upon.

Looking Ahead

Artificial intelligence is already influencing many aspects of clinical development, and its role will almost certainly continue to grow.

What is changing today is not simply the availability of new tools. It is the emergence of regulatory frameworks that define how AI can be used responsibly within regulated environments.

The conversation is moving beyond automation and toward evidence generation, scientific decision-making, and clinical innovation.

For the life sciences industry, this represents both an opportunity and a responsibility.

The challenge is no longer deciding whether AI matters.

Perhaps the most important lesson from recent FDA and EMA guidance is that artificial intelligence should not be viewed as a shortcut around scientific rigor. The organizations likely to benefit most from AI will be those that use it to strengthen existing processes, improve decision-making, and support experts rather than replace them. In regulated clinical development, trust remains just as important as innovation.