Explore the transformative impact of artificial intelligence (AI) on clinical trials. This article delves into the importance, regulatory status, applications, limitations of integrating AI in the drug development landscape and future aspects.


SwissCognitive Guest Blogger: Nancy Kapila, Team Lead, Roots Analysis – “AI in Clinical Trials: How Artificial Intelligence is Shaping Clinical Trials”


The current wave of advanced biologics, vaccines, and therapeutic drugs is shaping the pharmaceutical industry. Clinical trials play a crucial role in the success of novel therapeutic development, consuming almost 50% of the time and capital investment during R&D processes. It is a well-known fact that the success rate of clinical trials is only 10-20% due to inappropriate study design, insufficient patient recruitment, false predictions, and improper protocol adherence. Above all these factors, accurate data predictions play a major role in the success of drug development. To address these concerns, Artificial Intelligence emerges as a most disruptive technology, enabling automation, advanced analytics, and real-time data prediction—saving plenty of time wasted on data collection and patient recruitment for drug efficacy and safety analysis. Ironically, when combined with advanced technology such as digital twins or organ-on-a-chip, AI in clinical trials streamlines clinical trial data and reduces human errors in data collection.

Importance of AI in Clinical Trial

The conventional drug development process is extremely complex and time-consuming. On average, it takes around 10 years to develop a novel drug and a capital investment of around USD 2.5 billion. Clinical trial failure delays the supply of drugs into the market and puts a financial burden on pharma companies. During clinical trials an enormous amount of data has been generated and requires detailed evaluation. Analysis and management of large amounts of data can be challenging without robust analysis tools.  However, with AI-driven analysis software and technologies data analysis becomes more streamlined and eliminates the chances of errors. Artificial intelligence is a sub-field of computer science, and datasets encompass machine learning and deep learning to analyze and interpret data.

AI in clinical trials plays a massive role, automating multiple tasks that consume most of the time during R&D processes. With the integration of artificial intelligence, researchers can easily track things such as drug identification, patient selection, data analysis, identification of drug patterns, and adverse impact of drugs. Therefore, AI in clinical trials shortens the time required for data analysis and significantly improves the success rate with real-time prediction. AI analytical tools can be used as prediction models to accelerate clinical studies such as disease identification and suitable patients and support clinical study designs. According to Roots Analysis the global AI in clinical trials market size  is estimated to be worth $ 8.5 billion in 2035, growing at a CAGR of 16% during the forecast period 2023-2035

Regulatory Status on Using AI in Clinical Trials

The U.S. Food and Drug Administration is highly committed to data to ensure drugs are effective and safe for human consumption. AI in clinical trials undoubtedly plays an essential role in fetching accurate data. Based on this, the FDA facilitates innovation in drug development and adopts a flexible risk-based regulatory framework that enhances technology innovation while safeguarding patient health. As a crucial part of bringing flexibility, In May 2023, the FDA’s Center for Drug Evaluation and Research has issued an initial discussion paper in collaboration with the Center for Biologics Evaluation and Research and the Center for Devices and Radiological Health. This paper has been published to address necessary considerations for using AI in clinical trials and drug development such as data quality, human-led government and model development standards. The regulatory authorities continue asking for feedback on the importance of advancing regulatory science in this field.

Applications of AI in Clinical Trials

AI in clinical trials helps to uncover valuable data insight during drug development that remains hidden. A clinical trial involves data analysis, patient recruitment, documentation and other data validation procedures that are crucial to get approval from the FDA.  AI-enabled technologies are therefore becoming a crucial part of the critical trials in the following areas.

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1. AI in Clinical Trial Design

The adoption of AI in clinical trials by biopharma companies brings innovation to trial designs, effortlessly increasing the analysis of drug discovery data collected during clinical trials. AI-driven analytical tools help in quick comparison between current and past results of clinical trials. Integrating AI-enabled technologies also supports patient programs, post-market surveillance and has unparalleled potential to analyze, organize, or collect data generated during clinical trials. In simple terms, clinical trial design becomes more simplified and enables extraction of meaningful information such as drug failure, and adverse impact of drugs.

2. Site Selections

The most crucial aspect of clinical trials is the selection of highly functional investigator sites. Many parameters are taken into consideration when deciding investigator sites, such as administrative procedures, experienced clinicians, disease understanding, and resource availability. These qualities must be considered while selecting the site, as they influence data quality, integrity, and study timelines. AI in clinical trials helps pharmaceutical companies identify targeted sites and qualified investigators and collect evidence to meet regulatory standards. This ensures the clinical trial process adheres to Good Clinical Practice requirements.

3. Patient Enrichment, Enrolment and Recruitment

AI-driven transformation helps to improve patient selection and enhance clinical trial effectiveness through the proper analysis, mining, and interpretation of data through multiple resources. When combined with big data analytical tools, artificial intelligence helps interpret sources such as electronic health records and medical imaging data, thereby helping in patient recruitment.

4. Patient Medication Adherence, Monitoring and Retention

AI in clinical trials helps in patient medication monitoring and management by automating the capturing process. Combining AI algorithms with wearable digital technologies enables real-time insights and continuous patient monitoring. Moreover, AI algorithms also help fetch data about treatment effectiveness and safety while analyzing the risk of dropouts, enhancing patient engagement and retention.

5. Accurate use of Operational Data to Enhance AI-based Clinical Trial Analytics

Clinical trials generate a high amount of operational data. However, functional data disparate systems silos may hinder companies from receiving a comprehensive view of clinical trials portfolio across different global sites. Gathering information from any source into a common analytics platform backed with open data standards may promote integration while offering insights into important indicators. When combined with data visualization tools, a self-learning system intended to make better predictions and recommendations over time can proactively provide users with trustworthy analytics insights.

Limitations of AI in Clinical Trials

Though AI in clinical trials possesses enormous benefits, several limitations still have to be addressed. One of the potential limitations is the lack of standardization and data quality. Artificial intelligence relies on quality data to accurately predict and identify clinical trial patterns. Hence, the AI algorithm may not provide accurate prediction if the data is biased, inconsistent, or incomplete.

  • Data security and privacy are major concerns when using AI in clinical trials. Patient data is crucial yet sensitive; it has been protected from being accessed by unauthorized parties. Clinicians must take appropriate steps to ensure complete privacy and security of patient data.
  • Regulatory considerations have to be followed before implementation of AI in clinical trials. Though regulatory authorities such as the FDA have given flexibility in using AI for data analysis, using AI in clinical trials still raises concerns about data accountability and decision-making.

Paving Into the Future Vision of AI in Clinical TrialsTrails

Despite limitations, AI in the clinical trials market continually evolves to adopt and implement advanced technologies to improve data prediction while adhering to safety standards. The ongoing innovations show that AI in clinical trials holds a promising future.

Digital Twins Facilitate Real-Time Prediction

Digital twins, when combined with AI algorithms, generate virtual images that resemble patients’ physiological characters. This helps to achieve real-time insight into drug effects and individual health and enables the study of adverse drug impact. Currently, several pharmaceutical companies have begun to adopt digital twin technology monitored by AI to predict biological responses based on biomarkers.

AI Helps With Documentation

In the future, AI algorithms may expedite the regulatory documentation process and speed up the introduction of new drugs into the market.

Protocol Generation

AI language programs help create the first draft of clinical trial protocols using data inputs from previous trials, published literature, and multiple medical resources.

AI In Clinical Trials: Future of R&D Processes

Pharmaceutical companies are all set to pave into the storm of AI to develop tailored therapies that help treat diseases. As more novel drugs and therapies enter clinical studies, implementing AI in clinical trials becomes more feasible and important to shorten the time for drug development. Furthermore, flexibility in the regulatory guidelines for using artificial intelligence and machine learning in drug development encourages biopharma companies to accept innovation in clinical trials and implement cybersecurity to prevent data breaching issues.

In the future, AI / ML may combine with computer simulations and use advanced computer modeling in the regulatory evaluation of therapeutic drugs. Therefore, increasing implementation of virtual trials leveraging innovative digital technologies and artificial intelligence to lessen the financial burden and time required for drug development—bridging the gap between drug discovery and market supply.

Although AI hasn’t been included in clinical trials too often, it has the potential to revolutionize the process of developing new treatments. AI applications might make Clinical trials faster, safer, and far less expensive. The aim of biopharma to more thoroughly integrate patient-centricity across the whole R&D process will be achieved in part by the potential of AI to enhance the patient experience.


  1. https://www.nature.com/articles/s41746-019-0148-3
  2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974218/
  3. https://www.forbes.com/sites/greglicholai/2023/10/04/
  4. https://www.rootsanalysis.com/reports/ai-based-drug-discovery-market.html

About the Author:

Nancy KapilaNancy Kapila is a seasoned pharmaceutical consultant with over 5 years of varied experience and a Master’s in Pharmaceutics from Panjab University. She excels in drug mechanisms and interactions. Her career highlights include collaborating with numerous pharmaceutical companies and offering strategic insights and guidance. Nancy stands out for her dedication to keeping abreast of pharmaceutical advancements, regulatory changes, and emerging trends. She believes in continuous learning to navigate the industry’s complexities and provide innovative client solutions. Fascinated by the role of data analytics in decision-making, Nancy delves into data to uncover patterns and opportunities, offering evidence-based recommendations for process optimization, product development, and operational efficiency. Her career is driven by a relentless pursuit of knowledge, passion for data insights, and commitment to leading pharmaceutical companies towards success in a dynamic industry.