The Benefits of AI in Clinical Trials for Patients

Artificial intelligence (AI) is reshaping the design, planning, and execution of clinical trials. New efficiencies, capabilities, and opportunities are emerging across the lifecycle from early study design to data collection and beyond. As complexity rises and resources tighten, AI offers a faster, more adaptive, and better-informed path forward.

The benefits for patients are expected to grow as AI evolves into a strategic partner that enhances human expertise. As these tools automate and improve trial design decisions, the patient experience becomes more accessible, streamlined, and less burdensome.

Increasing Understanding and Accessibility

Participating in a clinical trial is a major decision. Patients are often presented with complex documentation like informed consent forms and technical protocols that are difficult to understand. Clinical research relies on individuals willing to put their bodies on the line, but the information they receive can fall short in explaining expected outcomes.

AI helps bridge the gap in education and awareness that traditional materials may miss. Thanks to advances in large language models (LLMs) and chatbots like ChatGPT, patients can now upload these documents and receive summaries or key questions to raise with doctors or family. This helps them understand what participation will involve and weigh trade-offs between joining the trial and potential outcomes.

Patients are also using AI tools to improve their understanding of their condition or the trial process, which is an important step toward tackling persistent health literacy challenges. If overwhelmed by complex materials, patients may decline to participate, delaying studies for potentially life-saving treatments.

Reducing Patient Burden

Even before the first patient is enrolled, AI is improving trial design. Clinical development teams can analyze protocols to flag high-burden procedures that may not be essential to a study’s primary objectives or endpoints. This makes participation easier without compromising scientific integrity.

Patient feedback on their experiences, including subjective factors such as anxiety, fear, and pain, can be collected and used to calculate a “patient burden score”. These scores, when integrated into AI models, become valuable inputs for future study design.

A Tufts CSDD analysis found that patient burden in Phase II/III trials rose 39% from 2019 to 2024¹. Over half of participants found trials disruptive due to additional appointments, travel, or unclear value in certain procedures.

This is where patient centricity and operational performance align. Trials that are easier to participate in are more likely to recruit and retain patients, resulting in improved data quality and shorter timelines. Medidata’s Protocol Optimization solution uses machine learning to suggest procedure changes or visit schedules, forecasting their operational impact. By analyzing protocol documents, AI assesses design complexity, site and patient burden, and cost. It can also compare the study design to industry benchmarks and recommend changes, like removing procedures or adjusting visit frequency, while evaluating impacts on timeline and cost.

The Future of AI in Clinical Trials

We’re just beginning to uncover the many ways AI can improve the patient experience, both within and beyond the scope of a single trial. Looking ahead, there are exciting possibilities:

• Personalized AI Assistants
  Taking LLMs a step further, personalized models could retain a patient’s medical history, helping doctors and patients ask questions and receive tailored insights. These assistants may also support appointment reminders and trial-related tasks.
• Proactive Care and Prevention
  AI can analyze data from sensors, wearables, and medical records to detect anomalies, predict issues, and recommend preventive measures before a diagnosis is made.
• Addressing Health Disparities
  AI tools can identify disparities related to factors like zip code, transport, or food access. For instance, if an asthma cluster emerges in a neighborhood, resources can be directed for early intervention. If a cancer pattern appears, communities can be equipped with screening and prevention tools.
• Financial Transparency in Clinical Trials
  Patients must consider travel, childcare, food, housing, and time away from work when deciding to join a trial. LLMs could help patients understand these financial implications and plan for participation based on their needs.

Building Toward a More Human-Centered Future

AI is not just another technology trend. It is transforming how we engage with information and navigate healthcare. A critical priority moving forward is ensuring fairness, trust, and empathy in AI tools. That requires training models with accurate, representative, and high-quality data because AI reflects the inputs it is built on.

At Medidata, we are using historical trial data to train models that support smarter algorithm development. This rich dataset enables more intelligent trial design informed by years of industry insights.

Patients are already seeing real benefits. Participation is becoming more understandable and accessible. From simplifying documents to identifying risk and supporting prevention, AI empowers individuals to make better-informed decisions throughout their care journey.

This is the beginning of a future that is more personalized, equitable, and proactive.

Download the latest case study of how CROs are able to move from planning to enrolment of patients in a pre-trial, faster and smarter.

^{1. National Library of Medicine (2020), The research burden of randomized controlled trial participation: a systematic thematic synthesis of qualitative evidence}

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