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Author

Erica Manning

Erica Manning

Erica is the creative and language services manager at Imperial Clinical Research Services. She holds a JD from Michigan State University and is a licensed attorney in the state of Michigan. Erica joined Imperial in 2010 and during her tenure, she has overseen more than 1,600 graphic design and translation projects for global clinical research trials, primarily for patient recruitment and retention materials. She was involved in the development of Imperial’s translation services quality management system and certifications as a translation services provider. Erica is also a conference speaker and has spoken at numerous conferences including the Drug Information Association’s (DIA) annual meeting.

    Business InsightsPatient Recruitment and Retention

    Artificial Intelligence for Clinical Trials: Move Ahead, or Halt?

    by Erica Manning July 26, 2022
    written by Erica Manning

    This blog was featured on Clinical Trial AI Weekly.

    For centuries, science fiction writers have toyed with the endless possibilities of artificial intelligence (AI). And now we’re living the future those writers celebrated and also feared. How might AI apply in the realities of our day-to-day jobs, especially the use of artificial intelligence for clinical trials?

    First, let us establish a shared understanding of AI for the purposes of this post. Generally, AI refers to the process of building or programming machines to replicate human intelligence. Programs of the past merely completed tasks based on human-provided rules. Today, leveraging complex algorithms allows machines to learn, change, and adapt to new situations without direct programming. These applications need only be exposed to large quantities of data.

    What does artificial intelligence for clinical trials offer?

    Within AI, there are four main categories of functionalities:

    • Reactive machines – a machine with abilities to perceive situations within their task set and react to situations as they are presented.
    • Limited memory – trains a machine to make decisions based on past data.
    • Theory of mind – we haven’t gotten there yet, but this is the stage where machines can replicate and react to the thoughts and emotions of others.
    • Self-awareness – is purely hypothetical currently, however, this is where a machine develops an independent intelligence, where self-awareness drives a desire for autonomy.

    Today, we are exploring the young lands of limited memory. There is no shortage of people tinkering and testing “new tricks” we can get these machines to perform for us. Over the past decade, we’ve seen plenty of marketing and commercial applications. Software that uses our past behavior to predict and modify it for shareholder and political gain are hot topics.

    It’s a classic question of good vs. evil: AI opens the possibility to discover new insights into ourselves and use behavior modification for positive social change. But it can also exploit weaknesses in our institutional systems for personal gain. There is also the possibility that AI will become too big or too smart to control.

    Best uses of artificial intelligence for clinical studies

    AI has strong potential in patient recruitment and retention. The most tried and true applications are in marketing and behavior prediction/modification.

    Many clinical trials are engaging social media and marketing experts to improve their recruitment campaigns. Client relations management applications can be used to assist in patient retention in the same way companies in other industries use AI to retain clients.

    Critics suggest it may be unethical to use algorithms to drive behavior. Yes, there are important questions to ask:

    • Are algorithms used in marketing exerting undue influence by pushing participants into trials?
    • Is there enough educated skepticism protecting people from known marketing influence?

    A review and revision of the consent process could mitigate these risks.

    Data mining is more efficient

    Emerging technologies also use large data resources in electronic health information to target qualified participants based on inclusion/exclusion criteria. However, with past data mining methods, only structured databases with particular fields were useful. It was also labor intensive to create structured databases by taking multiple data resources and consolidating the information. Now, unstructured data with disparate fields and information can be used. This opens up an ocean of resources that weren’t useful 10 years ago.

    With emerging technology, AI can be used to sort through data from medical records and social media to determine geographic areas where the trial indication is more widespread. Content from social media forums, where patients discuss their conditions, can be mined to determine where patients are located for specific cohorts.

    Ensuring patient safety: are machines superior to humans?

    Adverse events are currently monitored through human data entry systems that have notification and escalation processes. Most of the action-based monitoring is still completed by human oversight. With machine learning, there is potential to teach machines how to take in past study data along with adverse event information to create predictive models. In the future, machines could flag trends in adverse events sooner than human oversight or even predict participants at higher risk so they can be monitored more frequently.

    There is also the potential that smaller actual patient data samples will be needed to predict safety and efficacy in the future. Using all past research data, it is possible machines could show us what the most efficient population sample comprises. This could reduce the overall time and costs associated with clinical research. It may also better include historically underrepresented participant demographics. Validation of these types of predictive programs appears to remain a long way off.

    Challenges in using artificial intelligence for clinical trials

    Some areas that present challenges for advancing the use of AI in medicine and clinical trials:

    • Electronic health records (EHRs) are not universal in their format or quality
    • Sometimes data is siloed in medical imaging or pathology systems in addition to the EHR
    • Regulations surrounding the use of data differ globally
    • Bias exists, for example, currently available genetic data is mainly from patients of European descent
    • Ethics committees and laws need to keep pace with the changes and advancements in AI use

    With time, diligence, and cooperation, these challenges can be reduced, and the use and applicability of AI in clinical studies will increase.

    Our responsibilities

    Technology, just like money or beauty, can be used to benefit or to exploit. With AI, let’s keep one eye on the dire predictions of those imaginative writers, poets, and moviemakers, and another eye on the potential for good.

    For now, it is up to us to drive the technology we develop and use to right past wrongs and improve the overall health and lives of others. Our investment choices today will drive the reality of our future.

    I’m sure that when applying the use of artificial intelligence for clinical trials, if we keep patient safety and overall well-being top among our priorities, we’ll fair well. What are your thoughts? Scroll down to the comment box to let me know what you think. Also, check out our blog about using machine translation for clinical trial materials.

    July 26, 2022 0 comment
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  • Business Insights

    3 Surefire Ways Clinical Trial PMs Can Tame Their Inbox

    by Erica Manning March 15, 2022
    by Erica Manning March 15, 2022

    Email is a great thing: it allows busy clinical trial project managers to juggle multiple schedules, protocols, and teams in different time zones all over the world and still keep projects moving. Email chains keep a repository of time-stamped information and show documentation of discussions…

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  • Translation of Study Materials

    Better Instead of Worse: 3 Tips for Managing Translation Document Review

    by Erica Manning August 24, 2021
    by Erica Manning August 24, 2021

    The documents we create for clinical trials are collections of words. Reviewing these documents can be a straightforward process, with the final draft approved after one or two rounds of changes. In clinical trial translation management, sometimes a document is improved by the edits, sometimes…

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  • Site Materials & Logistics

    How to Take the Pain Out of Study Archiving

    by Erica Manning March 8, 2021
    by Erica Manning March 8, 2021

    Research record retention requirements, including the length of time files must be retained, are mandated by FDA regulations, ICH guidelines, non-U.S. regulatory agencies, and the sponsoring company’s SOPs. After a study closes, retention requirements are minimally two years but often exceed 15-30 years. The common requirements for storage of research patient case histories and essential documents (TMF) include:  Continual secure protection with limited access…

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  • Translation of Study Materials

    Keep Mother Languages Alive!

    by Erica Manning February 23, 2021
    by Erica Manning February 23, 2021

    Many people can take their language skills for granted (even if it is just one). Language gives us human beings a chance to express our thoughts, ideas, and feelings to one another. This allows us to understand each other, empathize and ultimately feel connected. Even…

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  • Business InsightsClinical Trial Writing and Design

    Process & Personalities: Tips for Clinical Trial PMs Working With Designers

    by Erica Manning February 21, 2019
    by Erica Manning February 21, 2019

    Working on a clinical trial creative design project with graphic designers can feel challenging. Typically, careers in clinical trial project management or operations attract a different personality type than people who are attracted to artistic fields. Differences in communication style and work environment preferences can…

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  • Clinical Trial Writing and Design

    Your Clinical Trial Branding Project: Questions to Ask Before Starting

    by Erica Manning January 9, 2019
    by Erica Manning January 9, 2019

    In this blog, we’ll discuss the best strategies for planning ahead before starting your clinical trial branding initiative. Planning ahead is important for any project but especially for creative ones. Here are tips to help get your branding project started off right: Define your audience…

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  • Business InsightsClinical Trial Writing and Design

    4 Tips for Writing for Patients in Clinical Trials

    by Erica Manning June 13, 2018
    by Erica Manning June 13, 2018

    Writing for patients can be tricky. Now, layer in the unique challenges of those participating in clinical research trials. Intimidated? Don’t be. By sticking to a few parameters, you can write content for clinical trial participants that is purposeful, engaging, and doesn’t cause a compliance…

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  • Business InsightsClinical Trial Writing and Design

    Writing Clinical Trial Content by Committee: Getting Everyone on the Same Page

    by Erica Manning May 31, 2018
    by Erica Manning May 31, 2018

    The more the merrier? Sure, there are many times when that holds true, but not when it comes to having multiple players contribute to writing clinical trial content. Do you sometimes feel like your documents are constantly stuck in the review process? You are likely…

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  • Business InsightsTranslation of Study Materials

    Translation Agencies – Will the Future Need Us?

    by Erica Manning November 30, 2017
    by Erica Manning November 30, 2017

    Returning from the Association of Translation Companies (ATC) Language Industries Summit 2017, I reflected on an emerging theme that threaded across the presentations and discussion topics. Like the gas lamplighters of the 1800s, whose numbers dwindled as electricity gradually eliminated the need for most of them…

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