The Benefits of AI in Healthcare: Reshaping Clinical Trials for Patients

Artificial intelligence is reshaping the design, planning, and execution of clinical trials. New efficiencies, capabilities. and opportunities are emerging everywhere: from early study design to data collection, trial operations, and beyond. As complexity increases and resources tighten, AI offers a new path forward—one that’s faster, more adaptive, and better-informed. 

Significant benefits for patients are expected to expand as AI tools evolve into strategic partners enhancing human expertise. As use of these tools to automate and improve trial design decisions increases, the patient experience will become more accessible, streamlined, and less burdensome.

Greater Access, Less Burden, and a Voice in the Process

Participating in a clinical trial is not an easy decision. Patients are presented with complex documentation ranging from informed consent forms to protocols that are technical and difficult to understand. The advancement of clinical research depends on the willingness and trust of these individuals to put their bodies on the line, but the information they receive often fails to clearly explain the anticipated outcomes.

Increasing Understanding and Accessibility

Thanks to advances in large language models (LLMs) and chatbots like ChatGPT, patients can now upload these documents and request summaries or key talking points to bring up with their doctors or families. They can get a clearer idea of what the experience will look like, developing a stronger understanding of the tradeoffs between their participation and the results of the trial.

“One of the big ways we're seeing patients operate on their own is using AI tools to help improve what they understand about their condition or the clinical trial experience that they're in.”

– Alicia Staley, Chief Patient Officer, Medidata

This is a significant step toward increased accessibility while helping tackle ongoing health literacy challenges. If patients feel overwhelmed or intimidated by the complexity of the information presented, they may decline participation—cumulatively resulting in delayed studies for treatments that could be saving lives.

“AI has really stepped in to fill a critical need around education, awareness, information,” says Alicia Staley, Chief Patient Officer, Medidata. “And they're just not getting that in a way that makes sense for them [from provided trial documentation]”.

Reducing Patient Burden

Even before the first patient is enrolled, AI is already helping improve the trial experience. Clinical development teams can now use AI to analyze planned protocols and identify high-burden procedures that aren’t essential to the trial’s primary objectives or endpoints—making it easier for patients to participate without compromising scientific integrity. 

Data from patients about their experiences, including subjective, unquantifiable factors such as anxiety, fear, and pain, can be collected and used to calculate a “patient burden score.” When integrated into an AI model, these scores become powerful inputs for future protocol design.

A Tufts CSDD analysis found that participation burden in Phase II/III trials rose 39% from 2019 to 2024. Over 50% of trial participants describe participation as disruptive to daily routines due to factors like extra appointments, travel, and not knowing the value that specific procedures contribute to the study.

This is where patient centricity and operational performance come together. Trials that are easier to participate in are more likely to recruit and retain patients, with improved data quality and reduced timelines. 

Medidata’s Protocol Optimization solution already employs machine learning to suggest changes to procedures or visit schedules and forecast their operational impact.

“By analyzing a protocol document, we can assess the design complexity including the patient burden, site burden, and cost. AI can then compare the study design relative to other comparable industry protocols and recommend certain changes—like removing a procedure or decreasing the frequency—and explore what impact those changes would have on the patient, as well as the study timeline and cost.”

– Rob Buka, Sr. Director, Product Management, Medidata

A Promising Future

We’re only just beginning to identify the many ways AI can improve the patient experience—both within and far beyond the scope of a single trial. Looking ahead, there are substantial opportunities.

Personalized AI Assistants

Taking LLMs a step further, personalized language models may emerge that retain a patient's complete medical history, allowing doctors and patients to ask questions and gain new insights. Additionally, these language models could help notify patients about upcoming appointments and assist with reminders to complete various tasks or activities for the trials they’re participating in or treatments they’re receiving.

Proactive Care and Prevention

AI can analyze data from various sources (such as sensors and wearables tracking various data, or hospital medical records) to flag anomalies, predict potential health issues, and recommend preventative measures—far before a patient receives a diagnosis.

Addressing Health Disparities

A fascinating AI application in the greater healthcare ecosystem is the ability to identify and address health disparities related to factors like zip code, access to transportation, and food deserts, leading to more targeted interventions.

For instance, identifying an asthma pocket in a specific town or city enables the delivery of resources to those communities to raise awareness around asthma or seasonal allergies. If AI tools flag a particular zip code showing a high prevalence for cancer clusters, we can better equip residents with the information and tools they need to get screenings and pursue preventive behavioral changes.

Financial Transparency in Clinical Trials

Patients must consider numerous financial factors when participating in a trial, including travel, childcare, food, and hotel/housing costs based on site visits, potential career impact, and more. LLMs may become a powerful tool enabling patients to gain a more comprehensive understanding, providing a starting point for budgeting and scheduling against the realities of their individual situations and needs.

Artificial intelligence is more than just shiny “new” technology. It’s a full-on shift that’s impacting our relationship with information and fundamentally changing how we interact with the world around us.

As we push forward, a crucial priority is acknowledging and addressing AI’s limitations. Ensuring fairness, trustworthiness and empathy requires building and testing these tools with representative populations, and training AI on accurate, comprehensive data. When these tools learn from a particular set of use cases, the suggestions it offers will be based on that set of use cases, making volume, accuracy, and diversity even more important.

“At Medidata, we have a special AI program. We're trying to leverage these historical trials into a specialized data set that does the learning, supporting the AI algorithms in a way that's very closely tied to the history of clinical research. We think we can help customers better design a future trial because of the deep expertise, the knowledge, and know-how we've accumulated by running so many earlier trials in the industry.”

– Anthony Costello, CEO, Medidata

Patients are already seeing the benefits come to life with trial participation becoming more accessible and understandable. From simplifying complex documents to predicting health risks and addressing disparities, AI is empowering individuals and communities to make more informed decisions across their healthcare journey. This transformative set of capabilities offers a pathway to a future where patient care is more personalized, equitable, and proactive.

Learn more about Medidata’s Patient Experience and explore how AI is transforming trials for all.