AI in Clinical Trials: My Predictions for the Future
As someone who’s worked in FDA clinical trials in the medical device industry for over 15 years, I have firsthand experience of the challenges and inefficiencies of this expensive and complicated process. Today, I’d like to share my predictions on how AI will transform clinical trials, making them more efficient and cost-effective. The following predictions, while not an exhaustive list, are the ones that I am looking forward to implementing the most in my future studies.
Automating Clinical Document Generation
Among the most accessible and swiftly implementable solutions with AI is in the generation of critical clinical trial documents. Now, the time-consuming and meticulous task of clinical document creation can be automated, all while improving accuracy and significantly reducing manual labor. By leveraging natural language processing (NLP) algorithms, AI can autonomously extract relevant information from the protocol and generate comprehensive and compliant Informed Consent Forms (ICFs) in simple language. This not only expedites the ICF creation process but also ensures accuracy and adherence to regulatory standards.
In addition to auto ICF generation, AI can automate the creation of source documents and Clinical Outcome Assessment (COA) Forms directly from the protocol as well. This not only reduces the burden on study personnel but also minimizes the likelihood of errors introduced during manual data transfer. The result is a more streamlined and error-resistant documentation process.
Building Clinical Databases
Beyond creating documents, AI can perform more complicated tasks such as the time-consuming process of developing and validating Electronic Data Capture (EDC) systems. These tasks, often performed by teams of multiple specialists, can be significantly expedited with the integration of AI. AI can extract and extrapolate data directly from the protocol to quickly generate a study-specific EDC system and then validate it according to the current regulations. While human oversight will still be essential at every step, AI can assist in generating and validating EDC systems faster, thereby accelerating the study startup phase and reducing the number of team members required to execute it. This efficiency translates to substantial time and cost savings, which are key concerns in clinical trials.
Moreover, AI can play an impactful role in the design and construction of all clinical databases, not just EDCs but also eTMFs and ePROs. Its assistance spans across the spectrum, optimizing the development and validation of these essential databases, and can significantly shorten the clinical study startup timeline by several months.
Enhanced Efficiency in Data Management
One of the key areas where AI is set to make a significant impact is in Clinical Trial Data Management. The technology can swiftly verify and validate all entered data, reducing the likelihood of errors and expediting the overall data review process. Moreover, AI can autonomously generate queries for data managers, presenting them with a comprehensive set of potential issues that may require attention. This not only enhances the accuracy of data but also significantly reduces the time and resources traditionally spent on manual data validation.
Conclusion
While I’ve only listed three specific areas where AI can make a substantial impact, optimizing any one of these areas using artificial intelligence has the potential to save a company a substantial amount of time, human resources, and money. The benefits extend far beyond optimizing mundane clinical trial tasks like ICF generation, database construction, and data management; the integration of AI across all aspects of clinical operations could drastically slash timelines, streamline resource utilization, and reduce clinical trial related expenditures for any organization. This total approach to AI adoption promises transformative outcomes across the clinical trial landscape.
For those concerned about AI displacing roles in clinical research, the future isn’t as bleak as it might seem: while clinical research jobs may be reduced due to AI, they won’t completely disappear. Despite AI’s advancements, it remains susceptible to errors and requires expert human oversight to review its designs, recommendations, and queries. For the time being, every feature that AI assists with will still ultimately need a human to review, correct, and approve it. AI will be able to streamline clinical trial processes and simplify various job functions within the clinical research realm, but it won’t be able to entirely replace the need for human involvement just yet. Instead, it’ll likely reduce the required clinical headcount for a study, offering efficiency but still necessitating human management to ensure accuracy, ethical conduct, and the reliability of results. Ultimately, AI serves as a powerful tool that complements human expertise rather than substituting it entirely in the complex field of clinical research. In short, I can’t wait to see how AI will help me be quicker, more efficient, and cost-effective at my job. But, just remember, at the end of the day, it’ll still be me rolling up my sleeves and getting the work done … for now.
