Pharma is going hi-tech across the medicine lifecycle—not only in the search for new drugs, but also in clinical development, manufacturing, quality control and safety monitoring. AI is one part of the shift; regulators are also addressing how its use should be evaluated and overseen. The documented examples show where these technologies are being applied, but they do not establish that AI has already made drug development faster, cheaper or more successful across the industry.
What “hi-tech” means in pharmaceuticals
In this context, hi-tech means applying digital tools and new production methods to work that spans from early research to medicines already in use. AI and machine learning can help analyze data or support particular tasks. Manufacturing innovation can change how medicines are made and how their quality is checked. Neither category is a single technology, and an application in one task does not prove that it is suitable for every other task.
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The European Medicines Agency (EMA) describes AI as relevant across the medicines lifecycle, while the World Health Organization’s 2024 publication considers both potential benefits and risks of AI in pharmaceutical development and delivery. That lifecycle view is more useful than treating AI as a shortcut confined to drug discovery.
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Where technology is being applied
| Stage or function | What is changing | Documented context |
|---|---|---|
| Research and discovery | AI and machine learning are considered for work involving medicines research and development. | The EMA’s 2024 reflection paper covers AI use across the medicinal product lifecycle; FDA and EMA’s 2026 common principles also address research. |
| Clinical development | Digital and AI methods may support evidence generation and analysis during clinical trials. | The agencies’ 2026 principles include clinical trials. The sources cited here do not establish a general improvement in trial duration or success. |
| Manufacturing and quality | Innovative production approaches include AI-driven batch testing and small “mini-pods” intended to make medicines near the point of care. | These are examples in EMA’s 2024 annual report, alongside its Quality Innovation Group’s work on innovative manufacturing and quality control. |
| Safety monitoring and post-authorisation | AI is being considered for work after a medicine is authorised, including safety monitoring. | The EMA reflection paper spans through post-authorisation; the 2026 FDA-EMA principles address monitoring across phases. |
| Regulatory work | Agencies are developing guidance, principles and ways to engage with innovative methods and manufacturing. | FDA has a January 2025 draft guidance on AI supporting regulatory decision-making and an Emerging Technology Program for innovative manufacturing discussions. |
The table describes areas of application and regulatory attention, not a ranking of technologies or proof that every proposed use is validated. The FDA Emerging Technology Program is an agency engagement route, not an endorsement of a commercial product.
#1 Best Overall
A concrete example: AI-assisted liver biopsy analysis
In March 2025, EMA issued its first qualification opinion for an AI-based development methodology: AIM-NASH, a tool that helps pathologists analyze liver biopsies to assess the severity of metabolic dysfunction-associated steatohepatitis (MASH). EMA’s 2025 annual report describes this as the first time the agency considered AI-assisted generated data scientifically valid to support a marketing authorisation application.
That is a specific regulatory milestone for a defined methodology. It does not mean AI has replaced pathologists, independently diagnosed patients, or received blanket acceptance for other diseases or uses. Qualification of a methodology should not be confused with authorisation of a medicine.
How regulators are approaching AI
EMA’s lifecycle reflection paper
EMA first published its reflection paper on the use of AI in the medicinal product lifecycle on 30 September 2024. It applies to human and veterinary medicines and sets out considerations for AI and machine learning across the lifecycle. EMA’s 2024 annual report says the draft received more than 1,300 stakeholder comments during consultation. That number records consultation submissions; it is not an adoption rate or a measure of public opinion.
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On 14 January 2026, FDA and EMA published ten common principles for good AI practice in drug development. They address evidence generation and monitoring across areas including research, clinical trials, manufacturing and safety monitoring. The principles signal regulatory cooperation and expectations for responsible practice; they are not a blanket approval of every AI system or a substitute for applicable regulatory requirements.
FDA guidance and manufacturing engagement
FDA’s AI for Drug Development materials include January 2025 draft guidance on AI used to support regulatory decision-making. Because it is draft guidance, it should not be described as a final rule. FDA’s Emerging Technology Program separately gives industry a route to discuss innovative manufacturing technologies; FDA says its experience includes advanced analytical tools and modeling approaches. In the EU, EMA’s Quality Innovation Group works on regulatory challenges associated with innovative manufacturing and quality control. These programs address engagement and regulatory questions, rather than endorsing particular vendors.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence does—and does not—show
The official examples establish that agencies are considering AI across the lifecycle, that a particular AI-assisted pathology methodology has received a qualification opinion, and that regulators are engaging with innovative manufacturing. They do not establish a reliable industry-wide adoption rate or a measured, attributable gain in development speed, cost savings or drug success from AI.
Rank #4
That distinction matters because a promising use case is not the same as demonstrated impact. A system’s value depends on the task, the data it uses, how its performance is evaluated and the oversight around its output. The 2024 WHO publication likewise frames AI in pharmaceutical development and delivery in terms of both benefits and risks. Claims about a specific system should therefore be judged on its evidence and intended use—not on the fact that it uses AI.
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