How AI Models Can Actually Improve eTMF Management

By Donatella Ballerini, GCP consultant

AI has fundamentally reshaped industries, and the clinical trial sector is no exception. One of the most widely recognized and publicly available AI-driven tools is ChatGPT, an advanced natural language processing (NLP) model developed by OpenAI. Alongside other AI models such as Gemini, Claude, and DeepSeek, these technologies do more than just analyze, interpret, and generate text comparable to that of humans — they are increasingly being integrated into real-world problem-solving.

There’s no denying AI’s growing role in clinical research. So, what does this mean for documentation management, and more specifically, for electronic Trial Master File (eTMF) management? AI tools are not just passive responders; they act as intelligent assistants, helping professionals refine their thoughts, brainstorm new ideas, and boost productivity.

In my own experience, ChatGPT serves as a valuable collaborator, offering alternative perspectives, helping me think critically, and enabling me to complete tasks more efficiently. Of course, AI comes with its share of challenges, from data privacy concerns to hallucinations and ethical dilemmas. But the opportunities it presents, particularly in clinical trials, are too significant to ignore.

Let’s explore how AI is shaping eTMF management and what this means for the future of clinical research.

The Role Of AI Models In Transforming Clinical Trial Management

Clinical trial professionals manage an overwhelming volume of documentation to ensure regulatory compliance and uphold study integrity. Every trial generates thousands of records—from protocols, emails, informed consent forms to site communications and monitoring reports. Ensuring these documents are complete, accurate, and aligned with ICH-GCP and regulatory requirements is a formidable challenge, especially given the complexity of trial operations.

This is where AI-driven tools like ChatGPT can step in, offering intelligent support to streamline key aspects of clinical trial management. While AI cannot (and should not) replace human oversight, it has the potential to enhance efficiency and accuracy in the following ways:

1. Automating Documentation
   AI can assist in drafting, reviewing, and summarizing essential clinical trial documents. Imagine a study coordinator needing to generate a monitoring visit report or a deviation summary. AI can quickly provide a structured draft, reducing the time spent on administrative tasks and allowing teams to focus on study execution. Additionally, it can help maintain consistency across documents, reducing errors and versioning issues.
2. Enhancing Regulatory Compliance
   Keeping up with evolving regulatory guidelines can be daunting. AI can help track, interpret, and summarize key changes in ICH-GCP, FDA, and EMA regulations. For example, a regulatory affairs specialist might use ChatGPT to quickly retrieve updates on the latest FDA guidance on electronic informed consent, ensuring the documentation aligns with current standards without spending hours of manual research.
3. Improving Patient Communication
   Engaging with trial participants effectively is crucial for recruitment and retention. AI can assist in answering common patient queries, explaining complex trial procedures in plain language, and even generating simplified summaries of informed consent forms. A real-world use case? Some healthcare organizations have experimented with AI chatbots to guide patients through clinical trial eligibility criteria, reducing the burden on site staff while improving participant understanding.
4. Data Analysis & Interpretation
   AI’s ability to process large datasets enables faster insights into patient records, adverse event (AE) reports, and trial outcomes. Consider a situation where a clinical team needs to identify safety signals across multiple sites. AI can scan thousands of AE reports and highlight emerging trends to help investigators make data-driven decisions more efficiently.
5. Accelerating Decision-Making
   By summarizing key trial findings and generating reports, AI allows sponsors, CROs, and investigators to make informed decisions faster. For instance, an AI model could analyze investigator site performance data, flagging sites that may require additional monitoring or training based on protocol deviations, helping sponsors optimize trial execution.

While human expertise remains irreplaceable, leveraging AI to handle routine and time-consuming tasks allows clinical professionals to focus on what truly matters—delivering high-quality, compliant, and efficient trials.

How AI Models Enhance eTMF Management

The eTMF is the key element of clinical trial documentation, ensuring all essential records are collected, stored, and readily available for audits and inspections. However, the sheer volume of records, combined with complex filing structures, makes eTMF management a time-consuming and error-prone process. Misfiled records, missing metadata, and inconsistent indexing are common pain points that can delay inspections and increase compliance risks.

This is where AI models can make a significant impact. By assisting with document classification, quality control, summarization, and compliance readiness, AI models can help clinical teams manage eTMFs more efficiently and accurately. Let’s look at some key areas where AI can support eTMF operations:

1. AI as a Filing Assistant

Be honest. At some point, you’ve probably stared at a document, unsure where to file it. Misfiled records and incorrect metadata attribution are among the most frequent quality issues in eTMF management. These errors create confusion and risks, especially when documents are needed for an audit or, worse, for an inspection.

How AI Helps:
AI models can analyze document content and recommend the correct filing location based on predefined classification rules (e.g., the CDISC TMF Reference Model). This feature is useful for:

• Junior staff or new team members who may not be familiar with the TMF structure.
• Emails and correspondence, where determining the correct artifact category can be tricky.
• Multi-functional record that could potentially fit into multiple TMF sections or artifacts.

Instead of second-guessing where to file a record, users can receive AI-generated filing recommendations, reducing the risk of misfiling and improving overall TMF organization.

2. Identifying Record Issues Before They Become Compliance Risks

Ensuring TMF completeness is a regulatory expectation, but manually checking thousands of records to identify missing, duplicate, or noncompliant issues is an enormous burden.

How AI Helps:
When an AI model is -properly trained on predefined criteria, it can:

• Detects missing or incomplete eTMF records before an auditor or an Inspector flag them.
• Identifies overdue or noncompliant records, helping study teams take timely corrective actions.
• Highlights gaps requiring immediate attention, reducing the risk of inspection findings.
• Detects duplicate records, preventing unnecessary clutter in the eTMF.
• Identifies quality issues in scanned copies, such as illegible text or incorrect page orders.

For example, if a site monitoring visit report is missing a signature, AI can flag it before it becomes a finding during an FDA inspection. This proactive approach significantly improves eTMF quality and compliance, and it is what regulatory authorities expect from clinical researchers.

3. Automating Record Summaries

Compiling meeting minutes, study updates, and eTMF quality reports can be a tedious, manual process. AI can streamline these tasks by generating concise summaries and even visual reports for management.

How AI Helps:

• Automatically summarizes meeting discussions and action items, expediting documentation.
• Creates data-driven visual reports. Imagine needing to present eTMF quality trends for a high-risk study to senior management. Instead of manually creating a report, AI can extract key findings from eTMF reports and generate:
  • A graph or dashboard highlighting the most frequent quality issues.
  • An automated PowerPoint slide deck, making engaging presentations to share and communicate the results.
• Reduces manual effort in generating reports and compliance overviews.

This functionality allows teams to focus on decision-making rather than spending hours formatting reports.

4. AI-Powered Data Analysis & Audit Trail Review

Audit trail reviews are a regulatory requirement, but they are notoriously time-consuming. Manually reviewing thousands of system log entries to detect irregularities is a daunting task.

How AI Helps:

• Scans audit trails for anomalies, such as unauthorized access at unusual hours.
• Flags unusual records modifications, helping teams quickly identify potential compliance risks.
• Speeds up audit prep by summarizing key trends in audit trail logs.

For example, AI could detect a case where a user accessed and modified essential trial records at 2 a.m., prompting further investigation into whether this was a legitimate action or a potential issue.

5. Smarter Record Search & Retrieval

Finding a specific record in an eTMF can feel like looking for a needle in a haystack — especially if it was misfiled or indexed incorrectly. Often, keyword searches fail when metadata is incomplete/incorrect or a naming convention is not in place.

How AI Helps:

• Retrieves records based on content, not just metadata.
• Finds related records (e.g., linking protocol amendments with their corresponding approvals).
• Saves time for study teams by making eTMF searches more intuitive and efficient.

For instance, if an auditor asks for the most recent Investigator Brochure , AI can retrieve it instantly — even if it was misclassified — by analyzing its content and matching it to other related documents.

The Benefits Of Using AI Model In eTMF Management

Managing eTMF without AI is like ruling Westeros without a Master of Whisperers — you might get there eventually, but you’ll miss a lot of critical information along the way. ChatGPT is like your very own Varys (minus the political scheming), scanning massive amounts of data and whispering insights in your ear before an auditor or an inspector arrives. From increasing operational efficiency and reducing manual burdens, to cutting compliance costs and strengthening audit readiness, AI offers real, tangible value. It streamlines record handling, flags anomalies early, and helps craft a compelling eTMF story when it matters most. With these capabilities in play, AI models don't just enhance eTMF management, they transform it.

Up Next: “The Risks Of AI In Clinical Research From A Trial Management Perspective”

Stay tuned for part two of this series, where we’ll explore how to harness these benefits and mitigate the risks, to keep quality, compliance, and ethics at the forefront. Because even with a whisperer by your side, the key to ruling wisely is knowing how to listen.

About The Author:

With 17 years of experience in the pharma industry, Donatella Ballerini first gained expertise at Chiesi Farmaceutici in the global clinical development department, focusing on clinical studies in rare disease and neonatology. Later, in global rare disease, Donatella served as a document and training manager, where she developed and implemented documentation management processes, leading the transition from paper to eTMF. In 2020, she became the Head of the GCP Compliance and Clinical Trial Administration Unit at Chiesi, ensuring all clinical operations processes complied with ICH-GCP standards and maintained inspection readiness. In 2021, she joined Montrium as the head of eTMF Services, where she helps pharmaceutical companies in eTMF implementation and process improvement, and also works as an independent GCP consultant. Donatella has been a member of the CDISC TMF Reference Model Education Governance Committee since 2023 and the CDISC Risk White Paper Initiative since 2024.