AIforPharma.org

Introduction

⇒ A European regulatory science study identifies the top priorities for trustworthy AI in medicines, led by accuracy, reliability, robustness, data governance and lifecycle control, with direct implications for pharmaceutical manufacturing and GMP.

A new paper published on 13 August 2026 in Clinical Pharmacology & Therapeutics provides an important indication of where future regulatory science for artificial intelligence in medicines may be heading.

The study, Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance, was developed within the European medicines regulatory network and involved authors affiliated with the European Medicines Agency and several European regulatory organisations.

The researchers asked a particularly important question:

Which scientific problems need to be solved to enable trustworthy use of AI throughout the medicines lifecycle?

The results suggest that stakeholders are increasingly looking beyond general AI governance principles.

The major unresolved challenge is becoming much more practical:

How can we demonstrate that AI-generated results are sufficiently accurate, reliable, robust and trustworthy for regulated pharmaceutical use?

273 Stakeholders Across the Medicines Ecosystem

The study was based on a European-wide survey conducted by the Network Data Steering Group of the European Medicines Regulatory Network.

A total of 273 responses were collected from groups including:

• national competent authorities;
• pharmaceutical industry professionals;
• small and medium-sized enterprises;
• patients and consumers;
• healthcare professionals;
• contract research organisations;
• academic researchers;
• EU agency professionals.

The largest groups were national competent authority employees, representing 24% of respondents, and pharmaceutical industry professionals, representing 22%.

Importantly, most respondents already had at least some professional familiarity with AI.

The study therefore provides more than a general public perception of AI. It captures views from people directly involved in medicines development, regulation and evaluation.

Seven Areas of AI Regulatory Science Were Evaluated

The researchers identified 28 research questions grouped into seven domains:

• accuracy and reliability of AI tools;
• data governance, confidentiality and consent;
• ethics, fairness and bias prevention;
• regulation and oversight;
• research integrity and intellectual property;
• resources and support for AI use;
• impact on jobs and skills.

Respondents were asked to rank the most important challenges.

The result was particularly interesting.

Accuracy and reliability of AI tools emerged as the highest-priority domain by a substantial margin.

The Number-One Question: Can We Trust the AI Output?

The highest-ranked research question concerned how the accuracy and reliability of AI models can be ensured when they generate evidence or inform regulatory decisions, and how their limitations can be identified.

Within the accuracy and reliability domain, this question was ranked highest by 45% of respondents.

The second major issue was closely related:

How can AI remain robust and trustworthy when data changes, performance deteriorates over time, information is incomplete or inputs are misleading?

This brings several familiar AI lifecycle risks directly into the regulatory science discussion:

• data drift;
• model drift;
• performance degradation;
• incomplete information;
• unexpected inputs;
• misleading inputs;
• uncertainty;
• changing operating environments.

The implication for pharmaceutical companies is important.

Initial AI validation may demonstrate acceptable performance at one point in time, but it does not automatically demonstrate that performance will remain acceptable throughout the system lifecycle.

From Validation to Maintaining an AI State of Control

This finding is particularly relevant for GMP applications.

Traditional computerized system validation often asks:

Did the system meet its predefined requirements when it was tested?

For AI, another question becomes equally important:

Does the system continue to perform reliably under changing conditions?

This leads naturally toward lifecycle controls such as:

• ongoing performance monitoring;
• predefined performance limits;
• drift detection;
• periodic re-evaluation;
• change control;
• model version control;
• revalidation or requalification triggers;
• monitoring of failure modes.

The regulatory science priorities identified in the study therefore support a broader concept of AI assurance:

Validation should not only demonstrate initial fitness for intended use. It should also establish how continued fitness for use will be demonstrated.

For GMP applications, this is closely related to the emerging concept of maintaining AI in an algorithmic state of control.

Explainability Is Important – But the Question Is “When and How Much?”

The third priority within the accuracy and reliability domain concerned explainability.

The study asks:

When is AI explainability important for regulatory decision-making, and which methods can provide it without unnecessarily affecting model performance?

This is an important distinction.

The future regulatory expectation may not necessarily be:

Every AI model must always be completely explainable.

A more risk-based question could be:

What level of explainability is necessary for this particular intended use, decision and associated risk?

For example, explainability requirements could reasonably differ between AI used to:

• search regulatory information;
• identify possible trends;
• support deviation investigations;
• predict process behaviour;
• support product quality decisions;
• generate evidence used in regulatory submissions.

This would be consistent with a context-of-use and risk-based approach to AI assurance.

Data Governance Is the Second Major Priority

The second-highest overall research domain was:

Data governance, confidentiality and consent.

Among the important questions identified were how data used for AI-based medicines development can be made:

• secure;
• auditable;
• traceable;
• legally and ethically usable.

For pharmaceutical companies, this reinforces a fundamental principle:

AI assurance starts with assurance of the data used by AI.

An advanced AI model cannot compensate for data that are incomplete, poorly controlled, untraceable or inappropriate for the intended purpose.

In GxP environments this connects directly with established expectations for:

• data integrity;
• data provenance;
• traceability;
• security;
• access control;
• retention;
• auditability.

Bias and Transparency Remain Major Concerns

Ethics, fairness and bias prevention ranked third among the seven domains.

The study identified two particularly important questions:

• How can bias in AI models used in medicines development and evaluation be identified and reduced?
• How should the limitations and uncertainties of AI use be communicated transparently?

The second point may be particularly important in regulated environments.

AI output should not create an impression of certainty that the underlying model cannot justify.

A mature pharmaceutical AI system may therefore need mechanisms for communicating:

• uncertainty;
• limitations;
• confidence;
• conditions under which the model should not be used;
• situations requiring escalation to a human expert.

A Surprising Result: “More Regulation” Was Not the Highest Priority

One of the most interesting findings is that Regulation and oversight ranked only fourth among the seven domains.

This should not be interpreted as suggesting that regulation is unimportant.

The authors propose another possible explanation.

Horizontal frameworks such as the EU Artificial Intelligence Act already provide important elements of AI governance.

What remains unresolved are many of the scientific and methodological questions that legislation alone cannot answer.

For example:

How accurate is accurate enough?

How should robustness be demonstrated?

How should performance degradation be detected?

When is explainability necessary?

What validation methodology should be used?

How should uncertainty be communicated?

These are fundamentally different questions from simply determining whether an AI system is legally permitted.

For pharmaceutical companies, this distinction is important.

The next major challenge may not be another general AI policy. It may be the development of accepted scientific methods for demonstrating trustworthy AI performance.

The Ten Highest Regulatory Research Priorities

After weighting the research questions according to the importance of their respective domains, the study identified ten priority areas.

They concern:

• accuracy and reliability of AI-generated evidence;
• robustness under changing data, performance degradation and misleading inputs;
• appropriate AI explainability;
• legal basis and consent for secondary use of health data;
• security, auditability and traceability of AI data;
• identification and reduction of AI bias;
• transparent communication of AI limitations and uncertainties;
• gaps in guidance for benefit-risk assessment, pharmacovigilance and evidence generation;
• transparency and reproducibility of AI-enabled research;
• technical checks and quality controls required to ensure that AI systems are validated, secure and reliable.

One Priority Is Directly Relevant to Pharmaceutical Manufacturing

The tenth research priority is especially important from a GMP perspective.

The study asks which:

technical checks and quality controls are essential to ensure that AI systems used in clinical trials, manufacturing, safety monitoring and regulatory submissions are properly validated, secure and reliable.

This explicitly places manufacturing within the regulatory science agenda for AI assurance.

The question goes beyond conventional software validation.

For an AI application in pharmaceutical manufacturing, appropriate control could potentially require consideration of:

• intended use;
• model performance;
• independent test data;
• data integrity;
• robustness;
• security;
• human oversight;
• model changes;
• performance monitoring;
• drift;
• revalidation triggers;
• failure management.

These are likely to become increasingly important as AI moves from experimental applications toward routine GxP processes.

The Real Question Is Moving from “Can We Use AI?” to “Can We Trust It?”

Perhaps the most important conclusion of the paper is captured by the overall pattern of the results.

Stakeholders do not appear primarily concerned with whether artificial intelligence should be used in medicines development.

The more important question is:

How can AI-generated outputs be trusted when they influence evidence generation and regulatory decision-making?

This represents an important evolution of the pharmaceutical AI discussion.

The early discussion was largely:

Can AI be used in regulated pharmaceutical processes?

The emerging discussion is increasingly:

What evidence is necessary to demonstrate that AI is sufficiently trustworthy for a particular regulated use?

That requires moving from generic principles toward measurable scientific criteria.

What This Means for Pharmaceutical Companies

For pharmaceutical organisations developing AI governance frameworks, the study provides a useful indication of where attention should be focused.

A practical AI assurance model could increasingly be built around:

Intended Use → Risk → Data → Performance → Robustness → Explainability → Controls → Human Oversight → Change Control → Continuous Monitoring

This is broader than traditional computerized system validation.

It also suggests that the future of AI assurance in pharma may be less about creating one universal “AI validation procedure” and more about establishing a multi-layered control strategy appropriate to the intended use and associated risk.

The fundamental regulatory science question therefore becomes:

Can we demonstrate – with evidence – that this AI remains sufficiently reliable, secure and controlled for the decision or process it supports?

Regulatory Status Note

This publication is a scientific research article and does not constitute formal EMA guidance or a new regulatory requirement.

The authors explicitly state that the views expressed are their personal views and should not be interpreted as representing the official position of the regulatory agencies or organisations with which they are affiliated.

The study should therefore be understood as an important indicator of regulatory science priorities rather than as an enforceable regulatory framework.

Nevertheless, its importance is increased by the involvement of the European Medicines Regulatory Network and by the participation of regulators, pharmaceutical industry professionals and other stakeholders.

Source

Pinheiro L.C. et al. – Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance. Clinical Pharmacology & Therapeutics. First published 13 August 2026.

https://ascpt.online … oi/10.1002/cpt.70400

DOI:

https://doi.org/10.1002/cpt.70400

Introduction

⇒ FDA scientists propose “computable pharmacovigilance,” a framework for deciding which regulated pharmaceutical tasks can be automated by AI and which still require expert human judgment.

A new perspective paper published on 18 August 2026 in Frontiers in Drug Safety and Regulation provides an important new way of thinking about AI automation in regulated pharmaceutical processes.

The authors are scientists from the U.S. Food and Drug Administration, including the National Center for Toxicological Research and the Center for Drug Evaluation and Research.

The paper focuses specifically on pharmacovigilance, but the underlying concept may have much broader relevance for pharmaceutical Quality Systems and GMP applications.

The central question is not simply:

“Can AI perform this task?”

Instead, the authors propose asking:

“Can this task be translated into sufficiently explicit, auditable and verifiable computational steps?”

They call this concept “computable pharmacovigilance.”

Not Every Pharmaceutical Task Is Equally Suitable for AI Automation

The framework distinguishes between tasks that can be clearly formalised and tasks that depend heavily on professional judgment.

Examples of relatively computable activities include:

• checking whether required information is missing;
• detecting potentially duplicated safety reports;
• extracting predefined information from records;
• coding structured information;
• retrieving relevant information;
• performing defined rule-based checks.

Such activities have relatively clear:

inputs → processing rules → outputs → acceptance criteria

and can therefore be tested, benchmarked and audited.

Other activities are fundamentally more difficult.

The authors use case-level causality assessment as an important example.

Determining whether a medicine actually caused a particular adverse event can require integration of:

• temporality;
• alternative explanations;
• clinical context;
• pharmacological mechanisms;
• rechallenge information;
• professional medical judgment.

These activities cannot yet be reduced reliably to fully explicit computational rules.

The consequence is important:

AI suitability should be assessed task by task rather than system by system.

“Computability” Could Be a Useful Concept Beyond Pharmacovigilance

Although the paper concerns pharmacovigilance, the same reasoning could potentially be useful when evaluating AI use in GMP.

For example, some Quality System activities may be highly computable:

• checking whether required fields are completed;
• comparing a document against predefined requirements;
• identifying duplicate or recurring deviations;
• extracting equipment or batch information;
• checking dates, limits or predefined conditions;
• classifying documents against controlled taxonomies.

Other activities may remain much less computable:

• determining the true root cause of a complex deviation;
• assessing whether an unexplained event affects product quality;
• evaluating the adequacy of a CAPA strategy;
• making a final batch disposition decision;
• determining whether an unusual observation represents an unacceptable patient risk.

This suggests a potentially useful AI risk-assessment question for pharma:

How much of the intended task can actually be expressed as explicit, testable and auditable logic?

The less computable the task, the greater the likely need for expert oversight.

Large Language Models Are Not Automatically the Best Solution

Another important message from the paper concerns the current tendency to use large language models for almost every AI application.

The authors distinguish between:

• large models – general-purpose LLMs and foundation models;
• small models – task-specific machine-learning models;
• deterministic tools – rules, dictionaries, workflow logic and conventional software.

Large models offer flexibility and can work across multiple text-heavy tasks.

But they also introduce important disadvantages:

• hallucinations;
• variable outputs;
• prompt sensitivity;
• possible information loss;
• more difficult validation;
• more difficult auditability.

Smaller task-specific models can be easier to validate when the task has constrained inputs and clearly defined outputs.

The paper therefore argues that the future may not belong to one universal AI model.

Instead, the most practical architecture may be hybrid.

A Hybrid Architecture: LLM + Small Models + Deterministic Tools

One particularly interesting proposal is an architecture in which an LLM acts as an orchestrator but does not perform every regulated function itself.

The architecture could look like:

User / Reviewer

↓

LLM or AI Agent

↓

Controlled Tools / Retrieval / Rules / Small Models

↓

Structured Result

↓

Expert Review and Decision

The LLM can interpret the request and coordinate different tools.

But specific operations can be delegated to more deterministic components such as:

• validated rule engines;
• controlled terminology;
• structured retrieval;
• database queries;
• schema validation;
• task-specific ML models.

This architecture can reduce uncontrolled generative behaviour.

It can also improve auditability because intermediate actions and outputs can be recorded.

For regulated pharmaceutical applications, this may be a much more attractive architecture than asking one general-purpose LLM to perform the entire process.

An Interesting Finding: Bigger AI Was Not Always Better

The authors also report an illustrative comparison between a task-specific BioBERT model and a general-purpose Llama-4 model for extraction of safety-related information.

For the evaluated structured extraction tasks, the task-specific model performed better.

The authors do not present this as a universal comparison between the two technologies.

Instead, the example demonstrates a broader principle:

The most powerful general-purpose model is not necessarily the best model for a tightly defined regulated task.

For pharmaceutical companies, this is significant.

The correct question may therefore not be:

“Which is the best AI model?”

but:

“Which technical architecture provides the most reliable, verifiable and auditable performance for this particular task?”

“Trust but Verify”

The paper describes the boundary of AI automation using a “trust but verify” philosophy.

AI can increasingly perform routine, well-specified operations.

However, as the process moves from:

pattern recognition → interpretation → judgment → regulated decision

the requirement for structured verification and expert review becomes increasingly important.

The authors conclude that Generative AI does not currently mean the end of human pharmacovigilance.

Instead, the likely future is a:

Human–AI system

in which AI performs increasingly sophisticated operations while humans remain responsible for judgment-intensive decisions.

Why This Matters for GMP and Pharmaceutical Quality

This concept could be particularly useful when companies perform AI use-case assessments.

Instead of simply classifying an application as:

AI / non-AI

or:

GxP / non-GxP

an additional question could be introduced:

How computable is the task?

A possible assessment sequence could therefore become:

Intended Use → GxP Impact → Task Computability → AI Architecture → Verification Strategy → Human Oversight → Lifecycle Monitoring

This could help organisations determine whether a task should be performed using:

• deterministic software;
• a task-specific ML model;
• an LLM;
• a hybrid architecture;
• or primarily human expert judgment.

This may ultimately be more useful than assuming that Generative AI should replace existing software simply because it is more flexible.

Regulatory Status Note

The article was written by FDA scientists, but it is a scientific perspective paper and does not represent formal FDA guidance or FDA policy.

The authors explicitly state that the views expressed are their own and do not necessarily represent the official position of the U.S. Food and Drug Administration.

Nevertheless, the paper provides valuable insight into how regulatory scientists are thinking about AI automation, validation, auditability and human judgment in regulated pharmaceutical activities.

Source

Wu L., Xu J., Dang O., Ball R. – Does generative AI mean the “end of history” for pharmacovigilance automation? Towards a framework for the future of human-AI systems. Frontiers in Drug Safety and Regulation, 18 August 2026:

https://www.frontier … fr.2026.1846339/full

Introduction

⇒ PDA outlines how AI can strengthen pharmaceutical quality metrics and predictive oversight while remaining embedded within validated data, Quality Systems and human decision-making.

A new article published by the Parenteral Drug Association on 20 August 2026 provides a particularly practical view of how artificial intelligence could be integrated into pharmaceutical quality management.

The article, Using Artificial Intelligence and Metrics, was prepared as part of the PDA Quality Management Maturity Team’s series on metrics implementation. Its authors represent Bristol Myers Squibb, HarborView and Eli Lilly.

Rather than focusing on AI as a standalone technology, the article places AI inside an existing pharmaceutical Quality System.

This distinction is important.

AI as a Quality Intelligence Layer

The authors describe how increasingly contextualized and high-integrity data can allow AI to support more proactive quality and regulatory decision-making.

Potential applications include:

• real-time monitoring of quality metrics;
• earlier detection of deviations, trends and anomalies;
• predictive identification of potential quality problems;
• continuous process insights;
• dynamic prioritization of investigations based on emerging trends.

The concept therefore goes beyond using AI simply to summarize documents or automate administrative work.

AI could become an analytical layer over established quality data, helping Quality organisations identify signals earlier and potentially move from retrospective review toward predictive oversight.

But AI Cannot Compensate for Weak Data

One of the strongest messages in the PDA article is that AI-driven metrics depend on structured and reliable data.

The authors emphasize the importance of structured, validated data models so that metrics remain standardized, traceable and auditable.

This is particularly relevant in GMP environments.

An advanced algorithm applied to inconsistent, poorly contextualized or unreliable data does not create a mature quality system. It can instead accelerate incorrect conclusions.

For pharmaceutical companies, this suggests that AI implementation should often start not with selecting an AI model, but with evaluating:

• data quality;
• data structure;
• data ownership;
• metric definitions;
• traceability;
• data integration between systems;
• reliability of the underlying quality indicators.

AI Should Reinforce Existing Metrics, Not Replace Them

The PDA authors propose that AI-generated insights should be linked to existing quality metrics rather than creating an independent decision framework.

They highlight several important control principles:

• AI models should be validated as decision-support tools under defined conditions;
• outputs should be sufficiently explainable for quality and regulatory review;
• inputs, processing and outcomes should remain traceable;
• appropriate audit trails and system controls should be maintained;
• AI should be integrated into existing governance and management processes.

This creates an important boundary:

AI should complement the Pharmaceutical Quality System rather than become a parallel Quality System.

An Interesting Warning: Do Not Optimize the Wrong Metric

One particularly important part of the article concerns the risk of relying on a narrow set of metrics.

AI systems are very effective at optimizing measurable objectives.

But if an organisation measures the wrong thing – or relies too heavily on one indicator – optimisation can produce unintended behaviour.

For example, reducing investigation closure time could appear positive.

However, if the metric becomes dominant, it could unintentionally encourage faster but less thorough investigations.

Similarly, reducing deviations is not necessarily evidence of improved process performance if problems are being classified or reported differently.

The authors therefore recommend a multi-metric, context-aware approach, with metrics periodically reassessed and stress-tested against possible gaming or unintended effects.

This concept is particularly important for AI because algorithms may amplify the consequences of poorly designed performance indicators.

What This Means for Pharmaceutical Quality Systems

The article suggests a potentially important direction for AI implementation in pharma:

AI should not replace established Quality Management Review – it should make it more intelligent.

A future AI-supported Quality System could continuously analyze:

• deviations;
• CAPAs;
• complaints;
• OOS and OOT results;
• process capability;
• environmental monitoring;
• audit observations;
• supplier performance;
• training effectiveness;
• change controls;
• recurring quality signals.

Instead of reviewing each metric independently, AI could identify relationships between them and detect weak signals that are difficult to see during conventional periodic review.

But such a system would still require validated data, appropriate model controls, human oversight and clear accountability.

Why This Publication Is Important

Much of the current discussion around pharmaceutical AI focuses on validating algorithms.

The PDA article points toward another important dimension:

AI effectiveness may depend as much on the maturity of the surrounding Quality System and data architecture as on the performance of the AI model itself.

This is consistent with an emerging view that pharmaceutical AI should not be implemented as an isolated technology project.

Instead, it should become part of:

Data Governance → Quality Metrics → AI Analytics → Human Review → Management Decision → Continuous Improvement

The AI model is only one component of that chain.

Regulatory Perspective

The PDA publication is an industry article and does not establish a new regulatory requirement.

However, its recommendations align with several themes increasingly visible in regulatory discussions: defined context of use, trustworthy data, validation, traceability, explainability, human oversight and lifecycle governance.

For pharmaceutical companies considering AI in Quality Systems, the article provides a useful practical bridge between AI technology and established ICH Q10-style quality management principles.

Source

PDA – Using Artificial Intelligence and Metrics, published 20 August 2026:

https://pda.org/pda- … lligence-and-metrics

FDA Draft Guidance – Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products:

https://www.fda.gov/ … -drug-and-biological