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Pharmaceutical technology is changing more than how researchers find candidate drugs. AI and other advanced tools are being considered across the medicines lifecycle, from discovery and clinical development to manufacturing, quality control and safety monitoring. Regulators are setting expectations alongside that shift, but examples of use and regulatory guidance are not proof that every tool improves outcomes or is ready to operate without human oversight.
Where technology is entering the medicines lifecycle
AI and machine learning are not confined to drug discovery. The European Medicines Agency (EMA) describes their potential use across the lifecycle, and principles published jointly by the EMA and the U.S. Food and Drug Administration (FDA) in January 2026 address AI in research, clinical trials, manufacturing and safety monitoring.
| Lifecycle stage | How technology may be used | What the cited evidence establishes |
|---|---|---|
| Discovery and research | Support research and analysis used to identify or evaluate potential medicines. | Regulatory materials address AI use in this phase; they do not establish that AI has produced a general increase in successful drug candidates. |
| Clinical development | Assist work involving clinical-trial evidence and analysis. | The agencies’ principles cover this phase, but the cited sources do not quantify improvements in trial duration, cost or success. |
| Manufacturing and quality | Support production processes, analytical testing and batch quality work. | EMA reports examples including AI-driven batch testing and small production units near the point of care. |
| Safety monitoring and post-authorisation | Help analyze information relevant to a medicine after authorization. | These stages fall within the lifecycle described by EMA and the FDA-EMA principles; the cited sources do not establish a market-wide performance measure. |
The distinction matters: a regulator discussing an application, an agency program supporting innovation, and a validated use in a particular context are different kinds of evidence.
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Research and discovery
AI can be applied to research tasks that involve analyzing data or helping investigators assess possible medicines. EMA’s 2024 reflection paper considers AI and machine learning across the medicinal-product lifecycle, beginning with discovery. That scope shows regulators are addressing the technology early in development; it does not by itself demonstrate that a particular AI-generated candidate is safe, effective or more likely to succeed.
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Clinical development and evidence
Clinical trials are included in the FDA-EMA principles for good AI practice. In practical terms, a tool used to generate or analyze evidence needs to be evaluated in relation to its intended use and the evidence it produces. The principles are not a blanket approval of AI systems, and their existence does not establish that a trial using AI will be faster or more successful.
A concrete example: AIM-NASH
EMA’s 2025 annual report says the agency issued its first qualification opinion for an AI-based development methodology in March 2025. AIM-NASH helps pathologists analyze liver biopsies to assess the severity of metabolic dysfunction-associated steatohepatitis (MASH). EMA described this as the first time it considered AI-assisted generated data scientifically valid to support a marketing authorisation application. This is a specific qualified methodology, not evidence that AI has replaced pathologists or independently diagnoses patients.
What is changing in pharmaceutical manufacturing
Advanced technology is also affecting how medicines are made and checked. EMA’s 2024 annual report describes ultramodern factories, AI-driven quality batch testing and “mini-pods” designed to produce medicines near the point of care. These examples point to different manufacturing roles: improving or supporting process and quality work, and bringing smaller-scale production closer to where medicines are needed. The report does not establish that these approaches are in routine use across the industry.
Quality oversight and agency programs
EMA’s Quality Innovation Group addresses regulatory challenges involving innovative design, manufacturing and quality control. In the United States, FDA’s Emerging Technology Program (ETP) provides a route for the agency to engage with industry on innovative manufacturing technologies; FDA says its experience includes advanced analytical tools and modeling approaches. These are regulator programs, not endorsements of particular commercial products.
How regulators are setting expectations for AI
EMA’s lifecycle reflection paper
EMA first published its reflection paper, “Use of Artificial Intelligence (AI) in the medicinal product lifecycle,” on 30 September 2024. It covers human and veterinary medicines and sets out considerations for AI and machine learning across the lifecycle. EMA’s 2024 annual report says stakeholders submitted more than 1,300 comments during consultation on the draft paper. That figure describes the consultation process; it is not a measure of how widely companies use AI.
FDA and EMA’s shared principles
On 14 January 2026, FDA and EMA published ten common principles for good AI practice in drug development. They address the generation and monitoring of evidence across phases such as research, clinical trials, manufacturing and safety monitoring. The principles signal areas of shared regulatory attention, but they do not amount to approval of every AI tool or replace the need to assess a system in its specific context.
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FDA materials and their status
FDA’s “Artificial Intelligence for Drug Development” page lists agency materials, including January 2025 draft guidance on AI supporting regulatory decision-making, and references the FDA-EMA principles. A draft guidance document and jointly issued principles are not the same as a product authorization. Anyone making a regulatory decision should consult the current agency documents and their exact scope rather than infer detailed compliance requirements from a high-level summary.
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What the evidence does—and does not—show
The cited sources establish regulatory work, programs and particular applications. They do not establish a reliable industry-wide adoption rate or a broad estimate of time saved, costs reduced or medicines successfully developed because of AI. WHO’s 25 March 2024 publication discusses both benefits and risks of AI for pharmaceutical development and delivery, but it does not provide a current market adoption rate. Claims about faster development or lower costs should therefore be treated as possible benefits unless a specific, attributable result is provided.
Best Value
The practical measure of progress is not whether a pharmaceutical tool uses AI, but whether its use is appropriate for the task and its outputs can be evaluated and overseen. A qualified methodology such as AIM-NASH offers a concrete, bounded example; broader lifecycle principles show regulators are preparing for wider applications without asserting that every application is already proven.
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