What Clinical Trial Experts Think Will Happen With AI In 2024

As told to Abby Proch, executive editor of guest columns, Clinical Leader

Heading into 2024, as with any annual flip of the calendar, we anticipate there will be topics of conversation that will fade away, persist, and develop — in the clinical trials industry as in life.

And so, when Clinical Leader asked experts across the continuum about their thoughts for the new year, we got a little bit of everything. Many people opined on the pervasiveness of AI, whether that’s in patient recruitment or regulatory oversight, a few acknowledged the persistence of the decentralized trial in all its many forms, and others saw gains and changes coming in their own therapeutic areas. In part one of this preview of clinical trials trends for 2024, experts discuss the applications of AI in clinical research.

Nareen Katta, head of data science & analytics, AbbVie

Applications of AI in clinical trials include, but are not limited to, the ability to harness real-world data to design better clinical trial protocols, identify the appropriate trial sites based on the protocol design and the site’s prior experience, reduce monitoring costs by identifying operational and data integrity risks, streamline safety surveillance, enable precision medicine, automate aspects of data integration and standardization needed for regulatory submissions, and even automate some aspects of document generation.

From a patient perspective, AI can make it easier for patients to be better informed about clinical research and where clinical trials can be a viable option for their health condition as well as streamline their experience while participating in clinical trials. From a healthcare provider’s perspective, AI can help better match the patients to the appropriate clinical trials and reduce the administrative burden in conducting clinical trials. 

The innovation in this space will continue to evolve at an exponential pace. To stay on top of this, leaders should adopt and instill a culture of continuous learning and experimentation in their organizations. They should stay connected to the ecosystem through industry conferences and other networking opportunities. In addition, they need to adapt their talent strategy, including constant upskilling of existing staff while attracting talent with the novel skillset. 

Because clinical trial workers and stakeholders are the most knowledgeable about the complexities and nuances of clinical research, they should upskill themselves on the concepts of AI and play an active role in designing, developing, deploying, and improving AI solutions. Ultimately, I believe AI will serve as a force multiplier to clinical trial stakeholders so they can significantly amplify their impact.

Ramita Tandon, chief clinical trials officer, Walgreens

In 2024 and beyond, clinical trials will see a renewed commitment toward more structured inclusivity and equity in trial recruitment, diversifying based on race and location (urban vs. rural). Expect a dual strategy to accomplish this: AI for rapid patient population identification and a community-centric approach to address historical fears and mistrust in certain communities toward clinical trials.

This involves community-centered research, exploring nontraditional trial locations like retail pharmacies, and leveraging AI for precise patient identification. AI helps us to identify the right patient population faster, but the conversion will rely on community-centered engagement. Our approach ensures that technology plays a role in our ability to swiftly identify the right patient population while respecting the nuances of individual communities, unlocking inequities in clinical trials.

Charlie Paterson, clinical development expert and associate partner, PA Consulting

AI has huge potential to transform the end-to-end trials process, including playing a role in protocol design, recruitment, site ops, analysis, and reporting. Ethical concerns and regulatory uncertainty will play a significant role in influencing how quickly the industry is able to move, and there will be the potential for new organizations to break out in this space. These will be data-rich organizations that build their platforms with their partners to generate trust and that best solve the problem of validating continually evolving models.

Alyssa Ingurgio, clinical research nurse, Mayo Clinic

Embracing technology is a pillar of research nursing currently in the spotlight as intrigue about AI in healthcare, and thus clinical trials, grows. Both MIT and Standford School of Medicine have developed curricula on the subject. Efforts to strengthen site-CRO-sponsor partnerships are underway with CRN-developed, AI-supported offerings that streamline standardization and recruitment. Standardization projects have been discussed regarding the use of AI for source document development, as misinterpretations and inconsistencies with these tools are a major source of study errors. Recruitment projects have been discussed regarding the use of AI for identifying clinical trial candidates through EHR data, which has also prevented patients from falling through the cracks.

Dennis Hancock, CEO & president, Mountain Valley MD

Mountain Valley MD believes the biggest shift across the health and wellness landscape revolves around the rapid advances in artificial intelligence and machine learning that will undoubtedly play a more significant role in clinical research. As these technologies mature, they will drive an entire rethink of how we design, implement, and analyze trials. Machine learning can consume and analyze unfathomable amounts of data that could completely change the approach to predictive modeling, perhaps even eliminating the need for certain trials in the future