Exploring AI-Assisted Clinical Evaluation with Ethan Drower from CiteMed
Artificial Intelligence is changing how medical device companies approach documentation, information management, and regulatory activities.
One area attracting increasing attention is Clinical Evaluation.
AI-powered tools promise to support literature reviews, organize clinical evidence, generate structured documentation, and potentially reduce the time required to prepare Clinical Evaluation Reports (CERs).
But an important question remains:
Can AI actually write a Clinical Evaluation Report, or does the process still depend on experienced clinical evaluation professionals?
In this episode of Medical Device Made Easy, we welcome Ethan Drower from CiteMed to explore the practical possibilities and limitations of AI-assisted clinical evaluation.
The conversation follows the journey from preparing the necessary inputs to reviewing the finished documentation, examining where AI may provide value and where qualified human expertise remains essential.
Can Someone Without Clinical Evaluation Experience Use AI to Write a CER?
One of the most important questions surrounding AI-assisted clinical evaluation is whether these tools make the process accessible to professionals without specific clinical evaluation experience.
Could a QA/RA professional who has never written a Clinical Evaluation Report use AI software to prepare one?
And if so, where would they still need guidance?
The episode explores the difference between using software to generate documentation and possessing the expertise required to assess its scientific and regulatory adequacy.
While AI may assist with certain activities, interpreting clinical evidence, evaluating its relevance, and supporting conclusions require appropriate knowledge and judgment.
Preparing the Right Inputs Before Using AI
A useful clinical evaluation workflow begins long before opening an AI tool.
Manufacturers need to understand the device being evaluated and establish a clear foundation for the evaluation.
This includes:
Device information and characteristics.
Intended purpose and clinical claims.
Available clinical evidence.
Clinical evaluation strategy.
Documentation structure and templates.
The discussion also considers how document templates can influence AI-assisted workflows.
A clear and consistent structure may support efficient documentation, while poorly designed templates can introduce unnecessary complexity.
AI output depends heavily on the quality and organization of the information provided.
How Can AI Support Literature Review and Evidence Collection?
Literature review is a critical component of clinical evaluation and can involve significant effort.
Identifying relevant publications, screening search results, extracting information, and organizing evidence all require time and attention.
The episode explores how AI tools may assist with different stages of this process, including literature screening, summarization, and the preparation of evidence tables.
However, AI-generated recommendations should not automatically determine which studies are included or excluded.
Qualified evaluators must assess the relevance, quality, and applicability of the evidence.
The objective is to explore how automation can reduce repetitive work without weakening the scientific foundation of the clinical evaluation.
From Clinical Evidence to a Draft Clinical Evaluation Report
Once relevant evidence has been collected, the next challenge is transforming it into coherent clinical evaluation documentation.
AI may help organize information and prepare draft sections, but a Clinical Evaluation Report must do more than summarize publications.
It needs to connect the available evidence to the device's intended purpose, clinical claims, safety, and performance.
The conversation explores how AI-assisted workflows can support this process while maintaining traceability.
Important questions include:
Can every significant statement be traced back to its source?
Does the available evidence actually support the proposed conclusion?
What happens when the evidence is incomplete, conflicting, or insufficient?
These questions demonstrate why clinical evaluation remains an evidence-based professional activity rather than simply a documentation exercise.
A Convincing AI-Generated CER Is Not Necessarily a Good CER
Generative AI can produce professional-looking documentation.
But clear writing and confident conclusions do not automatically demonstrate scientific validity.
The episode examines how experienced professionals can evaluate AI-generated reports and identify potential problems.
These may include unsupported claims, missing information, inaccurate summaries, inappropriate interpretation of evidence, or conclusions that are not justified by the available data.
The quality of a Clinical Evaluation Report depends on the quality of its evidence and reasoning—not simply how well it is written.
Appropriate review and approval remain essential parts of the process.
Can AI Really Save Time and Resources?
Efficiency is one of the main reasons manufacturers are interested in AI-assisted clinical evaluation.
But where can meaningful savings actually occur?
The discussion explores activities that may benefit from automation, including literature screening, information extraction, evidence organization, and initial drafting.
It also considers the work that continues to require significant human effort, such as critical appraisal, interpretation, review, and approval.
Another important consideration is whether the benefits differ between preparing a first Clinical Evaluation Report and updating an existing one.
For manufacturers evaluating AI tools, the key is to measure practical value rather than assuming that automation will automatically reduce overall costs.
Training Teams and Introducing AI into Clinical Evaluation
Adopting AI-assisted clinical evaluation involves more than learning how to operate software.
Teams need to understand the clinical evaluation process itself, the limitations of the technology, and the controls necessary to maintain reliable documentation.
The episode explores how manufacturers can introduce these workflows through manageable pilot projects.
It also examines when existing AI tools may be sufficient and when a dedicated platform could provide additional support.
For experienced clinical evaluation writers, successful adoption may depend on demonstrating how the technology can complement their expertise rather than attempting to replace their established methods.
Discovering CiteMed's Approach
During the conversation, Ethan Drower also introduces CiteMed and discusses its approach to supporting clinical evaluation activities.
The discussion includes the types of workflows its platform supports, the professionals it is designed for, and the onboarding or training available to customers.
This provides an opportunity to understand how dedicated software solutions may fit into existing clinical evaluation processes.
Final Takeaway: AI Can Assist, but Clinical Judgment Still Matters
Artificial Intelligence has the potential to support clinical evaluation workflows by helping professionals manage information, organize evidence, and prepare documentation more efficiently.
However, clinical evaluation cannot be reduced to automatically generating a report.
It requires a clear evaluation strategy, appropriate evidence, scientific reasoning, traceability, and qualified review.
AI can help write the document. But qualified professionals must ensure that the evidence supports the conclusions.
For medical device manufacturers, the opportunity lies in combining the efficiency of AI-assisted tools with the knowledge and judgment of experienced clinical evaluation professionals.
🎙️ In this episode of Medical Device Made Easy, Ethan Drower from CiteMed helps explore what that balance could look like in practice.
Can AI really write your Clinical Evaluation Report? Discover where technology can help—and where human expertise remains indispensable.
Who is Monir El Azzouzi?
Monir El Azzouzi is the founder and CEO of Easy Medical Device a Consulting firm that is supporting Medical Device manufacturers for any Quality and Regulatory affairs activities all over the world. Monir can help you to create your Quality Management System, Technical Documentation or he can also take care of your Clinical Evaluation, Clinical Investigation through his team or partners. Easy Medical Device can also become your Authorized Representative and Independent Importer Service provider for EU, UK and Switzerland.
Monir has around 16 years of experience within the Medical Device industry working for small businesses and also big corporate companies. He has now supported around 100 clients to remain compliant on the market. His passion to the Medical Device filed pushed him to create educative contents like, blog, podcast, YouTube videos, LinkedIn Lives where he invites guests who are sharing educative information to his audience. Visit easymedicaldevice.com to know more. If you need help implementing QMSR or preparing your teams for FDA inspections, contact: info@easymedicaldevice.com
Links
Ethan Drower's linkedin: https://www.linkedin.com/in/ethandrower/
CiteMed: https://citemed.com/podcast-materials/
CiteMed's linkedin: https://www.linkedin.com/company/getcitemed/
Social Media to follow
Monir El Azzouzi Linkedin: https://linkedin.com/in/melazzouzi
Twitter: https://twitter.com/elazzouzim
Pinterest: https://www.pinterest.com/easymedicaldevice
Instagram: https://www.instagram.com/easymedicaldevice
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