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As more medical writers adopt AI to accelerate the creation of medical documents, like clinical trial lay summaries, maintaining best practices remains essential. Fact-checking AI-generated content, validating data accuracy, and cross-checking against regulatory guidelines are critical steps to ensure compliance and patient trust.

While AI has significantly reduced the time required to draft medical documents, particularly clinical trial lay summaries, it is not without limitations. AI could oversimplify complex findings or overlook subtle compliance requirements.

This is where medical writers play a vital role. By combining AI efficiency with human expertise, writers ensure that outputs remain accurate, regulator-ready, and patient-friendly. When used responsibly, AI-powered medical writing solutions not only enhance productivity but also help shorten the overall clinical trial cycle, bringing new therapies to patients faster.

What Is a Clinical Trial Lay Summary and Why Does It Matter?

A clinical trial lay summary is a plain-language document that explains the purpose, design, and results of a clinical study in a way that is accessible to the general public. Unlike technical reports written for regulatory authorities, a lay summary is intended for patients, caregivers, and non-specialist audiences.

These summaries play a crucial role beyond patient communication. They are valuable resources for healthcare professionals, policymakers, commissioners, and funders, enabling them to interpret findings more easily and apply them in decision-making. By making research transparent and understandable, clinical trial lay summaries help improve public trust, encourage participation in research, and ultimately support better health outcomes at both the individual and population level.

The Role of AI in Lay Summary Writing

The process of writing a clinical trial lay summary has traditionally been time-intensive, requiring medical writers to carefully interpret Clinical Study Reports (CSRs) and convert technical findings into plain language. Today, generative AI platforms are transforming this process by automating the extraction of essential data from CSRs and trial databases. Using advanced natural language processing (NLP), AI tools can restructure complex medical information into accessible, patient-friendly text without losing scientific accuracy.

This capability not only accelerates the drafting stage but also helps ensure consistency, readability, and compliance with regulatory standards. When paired with human expertise, AI becomes a powerful co-pilot for medical writers, enabling faster turnaround while maintaining accuracy and trustworthiness.

Best Practices for Creating AI-Assisted Clinical Trial Lay Summaries

While AI has shortened the time spent on creating lay summaries, speed should not be the cost of clarity, accuracy, and compliance. Human expertise remains essential to ensure that AI-generated content is reliable, understandable, and aligned with regulatory requirements. Some of the best practices to keep in mind include the following:

1. Start with Accurate Source Data

The quality of your lay summary depends on the accuracy of its inputs. Therefore, always begin with validated and well-structured Clinical Study Reports (CSRs) and trial datasets. Using standardized formats and data tagging allows AI platforms to extract information more efficiently and reduce the risk of misinterpretation.

2. Leverage Pre-Built Templates for Compliance

Configuring AI-driven templates around regulatory standards ensures compliance from the start. These templates should include placeholders for essential elements, such as tables, figures, and listings (TFLs), to maintain a consistent and audit-ready structure.

3. Optimise Language for Readability

AI can simplify complex medical jargon into plain language through natural language processing (NLP), but readability must always be verified. A well-written lay summary should be accessible to the general public, while still accurately conveying the study’s key information and outcomes.

4. Include Essential Sections

When preparing a clinical trial lay summary, it is helpful to include clear, relevant information that patients, caregivers, and the general public can easily understand. This includes:

• Acknowledgement of participants: Begin by thanking the individuals who took part in the study.

• General study information: Provide the study title, who conducted it, and details about the sponsor, funding sources, or any competing interests.

• Public involvement: Explain whether patients or members of the public were involved in shaping the research (e.g., number of people, their lived experiences, and their contributions).

• Study context: Share where and when the study took place and why it was necessary.

• Research focus: State the main questions or objectives the study aimed to answer.

• Participants: Describe who took part in the trial and any eligibility criteria.

• Treatments or interventions: Summarize what treatments, drugs, or interventions participants received.

• Adverse events: Report any medical problems, side effects, or adverse reactions that occurred.

• Study process: Outline what happened during the study in simple, step-by-step terms.

• Results: Present the main findings in clear, non-technical language.

• Impact: Explain how the study has contributed to improving patient care, advancing research, or shaping future treatment approaches.

• Next steps: Mention if additional research is planned.

• Further resources: Provide details on where readers can learn more about the study, like clinical trial registries, publications, or websites.

5. Validate with Human Review and Quality Checks

AI-assisted drafting should never be considered final. Medical writers play a crucial role in refining the tone, ensuring cultural sensitivity, and validating accuracy. Incorporating GenAI-driven quality control tools can further benchmark drafts against gold-standard references and highlight gaps in compliance or clarity.

Common Pitfalls to Avoid When Using AI for Clinical Trial Lay Summaries

AI offers enormous potential, but it also comes with risks if not applied carefully. Be aware of these common pitfalls:

• Over-reliance on AI without sufficient human validation, leading to inaccuracies or misinterpretations.

• Using generic templates that don’t account for specific regulatory or therapeutic requirements.

• Overlooking patient literacy and cultural differences, which can make summaries inaccessible or even misleading.

AI should be viewed as a powerful assistant, not a replacement for human judgment. With the right balance of automation and expert oversight, organizations can deliver lay summaries that are fast, compliant, and patient-friendly.

How AuroraPrime Simplifies Lay Summary Creation

When it comes to creating your clinical trial lay summaries, AuroraPrime is purpose-built to streamline and accelerate the writing process.

Key capabilities include:

• Auto-learn and generate: AuroraPrime adapts to your instructions, historical documents, and structured data to automatically produce high-quality first drafts.

• Integrated compliance tools: The platform combines GenAI technologies with regulatory expertise, an embedded clinical knowledge base, and rule-based algorithms to ensure outputs are accurate, relevant, and fully compliant across all documentation stages.

• Collaboration features: Role-based project creation, version tracking, and multi-user review make teamwork seamless and transparent.

Beyond these core capabilities, our AI platform for clinical documentation also supports multi-language translation and real-time formatting validation, further reducing bottlenecks in the lay summary drafting.

By cutting first-draft creation time and consistently producing regulator-ready documents, AuroraPrime allows pharma teams to shift their focus from manual drafting to strategic review, patient-centric communication, and faster decision-making. The result is shortened clinical trial cycles, earlier patient access to new therapies, and measurable gains in operational efficiency.

If you want to know more examples of how you can leverage AuroraPrime aside from clinical trial lay summaries, check out our guide on solving pharma R&D challenges with AI authoring tools. We also have a guide on how to automate document authoring for regulatory dossier submissions through AI.