Explainable AI in Life Sciences: What “Traceability” Actually Means to Regulators

Artificial intelligence is moving deeper into drug discovery, clinical development, pharmacovigilance, medical affairs, regulatory operations, and manufacturing. As adoption grows, regulators are asking a practical question: Can an organisation explain what an AI system did, why it produced a particular output, and whether that output can be trusted for its intended use?

This is where auditable AI output traceability becomes important.

For life sciences organisations, traceability is broader than simply keeping a record of an AI response. It involves understanding the system's context of use, data, model, inputs, outputs, human review, changes, and controls well enough to reconstruct and evaluate the decision-making process.

The regulatory direction is increasingly risk-based. In January 2025, the FDA published draft guidance proposing a credibility assessment framework for AI models used to support regulatory decisions about drug and biological product safety, effectiveness, or quality. In January 2026, FDA and EMA also published ten shared principles for good AI practice in drug development, including clear context of use, data governance, documentation, performance assessment, lifecycle management, and clear essential information.

For teams building explainable AI life sciences workflows, the message is clear: AI needs an evidence trail.

What Does AI Traceability Actually Mean?

Traceability means being able to follow the path from the information entering an AI system to the output it produces and the human action that follows.

A simplified traceability chain looks like this:

Source data → Input → Model/version → Processing → AI output → Human review → Decision/action → Record

Each stage can matter depending on the AI system's context of use.

Consider an AI system that summarises clinical literature.

A weak audit record might simply show:

"AI generated a summary of 25 papers."

A stronger traceable record could show:

• Which 25 sources were used

• When they were retrieved

• Which source versions were available

• What instructions or prompt were used

• Which AI model generated the output

• Which model version was deployed

• What the system returned

• Whether a human reviewed the result

• What changes the reviewer made

• Which final version was approved

• Where the final output was used

Traceability Is Not the Same as Explainability

These terms are often used interchangeably, but they describe different concepts.

Explainability

Explainability concerns whether people can understand why an AI system produced a particular result.

For example:

Why did the model classify this patient record as high risk?

Traceability

Traceability concerns whether the organisation can reconstruct the relevant history surrounding that result.

For example:

Which data, model version, configuration, processing steps, and human review produced this result?

A system can therefore be traceable without being completely interpretable.

This distinction matters because some advanced AI models are difficult to explain internally.

EMA's reflection paper acknowledges that highly performing models can provide limited insight into their internal data representation and abstraction. It says black-box models may be acceptable in some circumstances when their performance and robustness justify their use, but recommends detailed information on model architecture, tuning, training metrics, validation and testing, monitoring, risk management, and explainability methods where possible.

In other words:

The harder a model is to interpret, the more important surrounding evidence and controls become.

Why Regulators Care About Traceability

Regulators ultimately care about the reliability and integrity of information used to support decisions involving medicines and patients.

AI introduces new variables into that process.

A conventional analytical workflow may have clearly documented:

• Data source

• Statistical method

• Software

• Analyst

• Version

• Review process

• Final result

An AI workflow can introduce additional variables:

• Model architecture

• Training data

• Retrieval sources

• Prompt or system instructions

• Model version

• Parameters

• External tools

• Generated output

• Human intervention

• Model updates

• Performance drift

The FDA's Risk-Based Direction

The FDA's January 2025 draft guidance is particularly relevant because it frames AI credibility around context of use.

Rather than treating every AI model identically, the proposed framework asks organisations to assess whether a model is credible for the specific purpose for which it is being used.

This is an important distinction.

An AI system used to summarise publicly available literature for internal research may require different controls from a model whose output contributes directly to a regulatory submission or a safety-related decision.

The higher the potential impact, the stronger the evidence and governance expectations are likely to become.

That is why organisations should avoid asking:

"Is our AI compliant?"

A better question is:

"Is this AI system appropriately controlled and supported by evidence for its specific context of use?"

What Should Be Traceable?

There is no single universal traceability checklist for every AI application.

However, a practical framework for life sciences organisations can include seven layers.

1. Data Traceability

Teams should understand where relevant input data came from.

This may include:

• Scientific publications

• Clinical trial datasets

• Real-world data

• Internal documents

• Regulatory databases

• Safety reports

• Structured databases

• Patient or healthcare professional information

2. Model Traceability

The model itself needs an identifiable history.

Important information can include:

• Model name

• Model version

• Deployment date

• Configuration

• Relevant changes

• Validation status

• Performance characteristics

• Intended use

• Known limitations

3. Prompt and Instruction Traceability

Generative AI introduces another layer: instructions.

Two users can submit the same source documents but provide different instructions and receive different outputs.

Therefore, in higher-risk applications, teams may need to preserve relevant:

• System instructions

• User prompts

• Templates

• Workflow configuration

• Retrieval instructions

• Output requirements

4. Output Traceability

The final output should be identifiable.

This can include:

• Generated response

• Date and time

• Model version

• Source references

• Confidence or performance information where applicable

• User identity or role

• Review status

• Final approved version

5. Human Review Traceability

Human oversight is a central part of responsible AI in regulated environments.

The important question is not simply:

"Was a human involved?"

It is:

"What did the human actually review and decide?"

A useful record may distinguish between:

AI-generated output

and

Human-reviewed final output

The reviewer may:

• Accept the output

• Correct it

• Reject it

• Request additional evidence

• Escalate an issue

• Change the final conclusion

Those actions can become part of the audit trail.

The EU AI Act similarly places emphasis on transparency, human oversight, and logging for high-risk AI systems. Its requirements include automatic recording of relevant events and sufficient transparency to allow deployers to interpret outputs and use them appropriately.

For life sciences organisations operating across jurisdictions, this broader regulatory direction is worth monitoring.

6. Decision Traceability

An AI output is not necessarily the final decision.

A Medical Affairs professional may review a literature synthesis.

A clinical scientist may evaluate a model-generated analysis.

A regulatory professional may assess an AI-supported document.

A safety team may review a potential signal.

The organisation should be able to distinguish:

What AI suggested

from

What the qualified professional decided.

This distinction is essential for accountability.

It also helps prevent the common assumption that using AI transfers responsibility from the human organisation to the technology provider.

7. Change Traceability

AI systems change.

Data changes.

Models are updated.

Retrieval systems are modified.

Prompts evolve.

External tools are added or removed.

Therefore, traceability should extend across the system lifecycle.

EMA recommends lifecycle management and monitoring of AI/ML applications, including attention to performance degradation and model changes.

A practical change log might capture:

This creates a historical record of the AI system rather than treating it as a static application.

AI Traceability in Clinical Development

Clinical development provides a useful example of why traceability matters.

AI may support:

• Patient recruitment

• Protocol development

• Clinical data analysis

• Medical coding

• Statistical analysis

• Literature review

• Safety monitoring

• Trial feasibility

• Evidence generation

AI Traceability in Regulatory Affairs

Regulatory teams are likely to face increasing pressure to understand AI-assisted workflows.

AI can help with:

• Submission content review

• Regulatory intelligence

• Document comparison

• Health authority correspondence analysis

• Label review

• Regulatory research

• Submission tracking

For the latter, teams should establish stronger controls around:

• Source verification

• Version control

• Human review

• Approved data sources

• Output retention

• Change management

• Audit trails

This is where AI traceability pharma becomes more than an IT concern.

It becomes part of regulatory operations and quality management.

What Does "Auditable AI" Really Mean?

An auditable AI regulatory workflow should allow an authorised reviewer to answer several basic questions.

What was the AI being used for?

The intended context of use should be documented.

What information did it use?

Relevant inputs and sources should be identifiable.

Which system generated the output?

The model and relevant version should be recorded.

What did it produce?

The generated output should be retained where appropriate.

Who reviewed it?

Human involvement should be identifiable.

What changed?

Material edits and system changes should be documented.

Why was the final output accepted?

The relevant approval or decision process should be clear.

This is the difference between having an AI application and having an auditable AI workflow.

Traceability Does Not Mean Recording Everything

There is a potential misconception that regulatory traceability means organisations must record every technical event indefinitely.

That is not necessarily the practical objective.

Good governance should be risk-based.

A low-risk AI tool used for brainstorming may need relatively lightweight controls.

A system supporting a high-impact regulated decision requires substantially stronger controls.

The FDA's proposed framework reflects this principle by focusing on the credibility of an AI model for its specific context of use.

EMA similarly recommends a risk-based approach, including stronger performance assessment and monitoring where patient risk or regulatory impact is higher.

The practical objective is therefore:

Capture the evidence necessary to demonstrate appropriate control for the intended use.

Building an Explainable AI Framework for Life Sciences

A practical framework can be organised around five questions.

Context

What is the AI being used for?

Define the intended use, users, outputs, and potential consequences.

Evidence

What supports the AI output?

Identify data, sources, validation evidence, and relevant performance information.

Traceability

Can the workflow be reconstructed?

Record the relevant inputs, model, configuration, output, review, and changes.

Oversight

Who remains accountable?

Define human responsibilities, escalation procedures, approval points, and override mechanisms.

Monitoring

How will the organisation know if the system changes or deteriorates?

Establish performance monitoring, incident management, drift detection, and periodic review.

This framework aligns closely with the direction of the joint FDA-EMA principles, which include human-centric design, risk-based approaches, clear context of use, data governance, model development practices, performance assessment, lifecycle management, and clear essential information.

How AI Platforms Can Support Traceability

The technology architecture matters.

An AI platform designed for regulated life sciences work should ideally provide mechanisms for:

• Source-linked answers

• Document provenance

• Model/version identification

• User access controls

• Audit logs

• Review workflows

• Output retention

• Permission management

• Change tracking

• Evidence citation

• Human approval

The purpose is not to make the interface complicated.

It is to ensure that the underlying workflow remains defensible when the stakes are high.

Pienomial's Knol AI is designed to support AI-assisted research and intelligence workflows, while the broader Pienomial platform provides an environment for working with complex information across organisational use cases.

For life sciences teams, Pienomial's Life Sciences solution can support evidence and intelligence workflows where source visibility, structured analysis, and human oversight are important.

The value of this approach is not simply faster AI output.

It is creating a workflow where teams can understand and validate the information behind that output.

A Practical Traceability Checklist

Before deploying AI in a regulated life sciences workflow, teams can ask:

• Is the context of use clearly defined?

• Is the intended user population documented?

• Are approved data sources identified?

• Can important inputs be traced?

• Is the model version identifiable?

• Are relevant prompts or workflow instructions retained?

• Can outputs be reproduced or reconstructed where necessary?

• Are source references available?

• Is human review defined?

• Are reviewer actions recorded where appropriate?

• Are material system changes tracked?

• Is model performance monitored?

• Are limitations documented?

• Is there an escalation process for unexpected outputs?

• Are retention and access controls appropriate?

• Has the system been assessed for its specific context of use?

The answers will differ depending on the AI application.

But asking the questions early can prevent governance from becoming an afterthought.

The Future of Explainable AI in Life Sciences

AI adoption in life sciences will continue expanding.

The technology will become more capable, but greater capability will not eliminate the need for accountability.

If anything, it makes accountability more important.

The 2026 FDA-EMA principles show a growing focus on responsible AI across the medicine lifecycle, including evidence generation and monitoring.

Regulators are not necessarily asking organisations to abandon complex models.

They are asking organisations to understand how those models are used, assess their risks, document their performance, and maintain appropriate oversight.

That means the future of explainable AI life sciences is unlikely to depend on making every AI model completely transparent internally.

Instead, it will depend on creating enough surrounding evidence and governance to establish confidence in the system's intended use.

A powerful model without an evidence trail can become a compliance risk.

A powerful model with appropriate validation, documentation, monitoring, source traceability, and human oversight can become a much more useful enterprise tool.

Conclusion

Traceability is becoming one of the most important concepts in regulated AI.

It does not simply mean saving a chatbot response.

It means creating a defensible record of the circumstances surrounding an AI output: what the system was intended to do, what information it used, which model and configuration were involved, what it produced, how a human reviewed it, what decisions followed, and how the system changed over time.

The FDA's risk-based approach and the joint FDA-EMA principles point toward a model in which AI credibility depends heavily on its specific context of use, evidence, documentation, performance, lifecycle management, and human oversight.

EMA's guidance further reinforces the importance of explainability, monitoring, technical documentation, and risk management, particularly where black-box systems are used.

For pharma and life sciences organisations, the practical takeaway is straightforward:

Do not ask only whether an AI system produces a good answer. Ask whether you can demonstrate why that answer should be trusted.

That is what traceability ultimately provides.

It turns AI from an opaque source of generated content into a controlled, reviewable, and accountable component of a regulated workflow.

Frequently Asked Questions

1. What is AI traceability in life sciences?

AI traceability is the ability to reconstruct the relevant history behind an AI-generated output, including its context of use, inputs, model or system version, processing, output, human review, decisions, and material changes.

2. Why is traceability important for pharma AI?

Traceability helps organisations demonstrate that AI systems are being used appropriately and that important outputs can be reviewed, verified, and connected to their underlying evidence.

3. Is explainability the same as traceability?

No. Explainability focuses on understanding why an AI system produced a result. Traceability focuses on reconstructing the relevant information and events surrounding that result. They are related but distinct capabilities.

4. What does the FDA expect from AI used in regulatory decision-making?

The FDA's 2025 draft guidance proposes a risk-based credibility assessment framework for AI models used to support regulatory decisions about drug and biological product safety, effectiveness, or quality. The framework is centred on the model's specific context of use.

5. Does every AI tool need the same level of traceability?

No. Traceability and governance should be proportionate to the AI system's intended use, risk, and potential regulatory or patient impact.

6. What should an auditable AI workflow record?

Depending on the use case, this can include data sources, inputs, model and version, prompts or configuration, outputs, source references, human review, approvals, system changes, validation information, and performance monitoring.

References

[1] U.S. Food and Drug Administration (FDA). Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products. FDA's January 2025 draft guidance proposes a risk-based credibility assessment framework for AI models used to support regulatory decisions concerning drug and biological product safety, effectiveness, or quality.

[2] U.S. Food and Drug Administration (FDA). Guiding Principles of Good AI Practice in Drug Development. FDA and EMA's ten principles address human-centric design, risk-based approaches, context of use, data governance, documentation, model development, performance assessment, lifecycle management, and essential information.

[3] European Medicines Agency (EMA). Reflection Paper on the Use of Artificial Intelligence in the Medicinal Product Lifecycle. The reflection paper addresses explainability, model architecture, validation, monitoring, risk management, human oversight, and the use of AI across the medicine lifecycle.

[4] International Council for Harmonisation (ICH). ICH E6(R3) Good Clinical Practice. The guideline addresses computerised systems used in clinical trials, including system records, validation, audit trails, access controls, security, procedures, and training.

[5] European Union. Regulation (EU) 2024/1689 — Artificial Intelligence Act. The EU AI Act establishes requirements for high-risk AI systems relating to logging, traceability, transparency, and human oversight, providing an important broader regulatory reference for organisations deploying AI in regulated environments.