CROs and Artificial Intelligence: How AI is Changing Clinical Research

Technologies have the power to revolutionize and change industries. One emerging technology, artificial intelligence, has already impacted various fields, from content writing to film. And now, AI is changing the landscape of clinical research too.

These developments involve more than just automating data processes. By leveraging AI algorithms and machine learning, medical institutions and clinical research organizations (CROs) can transform the execution of clinical research and trials.

So – what is AI, and how is it changing clinical research?

What is Artificial Intelligence (AI)?

When we think of AI, we think of robots that act like humans or computer programs that have a “conscience.” It’s a concept largely associated with science fiction, but it’s fast becoming a reality all around us. These days, AI is a hot topic in multiple industries – even in clinical research.

Essentially, AI or artificial intelligence is a field combining computer science with expansive datasets, which allows for machine-enabled problem solving. AI encompasses machine learning and “deep” learning. AI, ML, and DL all rely on algorithms that make predictions or classifications based on input data (an act called “scraping”).

Take AI content generators like ChatGPT and Jasper as an example. These programs will “scrape” existing written content on the internet and use this data to generate paragraphs about a specific topic (called a “prompt”). This means that if you want to write about biotechnology, the program will scan all public-use data about biotechnology and then use the information it has gathered to construct written content.

AI in Clinical Research: Changing the Landscape

Of course, while AI writing and art generators are the better-known examples for the general public, AI also has its uses in other industries, such as clinical research. CROs can leverage these technologies to make the drug discovery and development process quicker and more efficient.

Wearable devices

As clinical trials shift to more decentralized models, the use of wearable medical technology has risen. These devices can track and log various health metrics such as heart rate, body temperature, blood pressure, glucose levels, and movement.

AI can support and improve the use of wearables in many ways. Besides automatically collecting and processing data inputs, it can also automate decision-making regarding device notifications. An AI program could also generate recommended patient actions based on patterns in their health data.

One pioneer in partnering wearable health devices with digital health intelligence is Digital Salutem. They leverage remote monitoring platforms and smart devices to transform healthcare.

Data processing for decentralized trials

There are several hurdles when it comes to decentralized clinical trials, one of which is data collection and processing. Since patients are off-site, they have to regularly and consciously submit their own participation data. This can bring up issues with patient compliance and data errors.

CROs and medical research institutions can leverage AI to solve these issues in several ways. They can create algorithms to analyze patient data and create decisions that will achieve a desired outcome – in this case, consistent patient compliance. AI can optimize and generate notifications that prompt patients to complete electronic clinical outcome assessments (eCOA) for a more reliable data pool.

Moreover, AI programs can assist patients in submitting their data by analyzing the quality of the data prior to acceptance. For example, an AI program can evaluate an image to see whether it fits the requirements of the clinical trial. It can then prompt the patient to retake the image with recommendations regarding image quality, such as lighting or angle. This limits the amount of insufficient or substandard submissions, thereby leading to fewer data processing errors.

Data mining and patient recruitment

Recruitment is a crucial stage for drug development and clinical trials. If recruitment is unsuccessful – or if research organizations cannot recruit fast enough – it leads to clinical trial failure. AI can assist in patient recruitment by parsing through population data to detect demographics and subgroups that will benefit the most from participating in the trial.

An AI algorithm can also help select locations for patient recruitment by examining data and identifying countries or regions where a disease is more prevalent. Moreover, it can analyze medical records for potential trial opportunities.

Lastly, AI algorithms can parse through existing information about the disease and cross-reference it with the clinical trial parameters to generate eligibility criteria. They can then simplify this criteria for presentation to potential participants.


A key step in clinical trials is testing drug safety and efficacy. While a drug eventually has to undergo human testing, researchers can employ biosimulation to run these tests virtually and gauge the human response to the medical compound.

Biosimulation is the use of math-based computer simulations to replicate a body’s biological processes and systems. Through AI and machine learning, the simulation creates predictions based on models and data, which researchers can use to gather information about the drug’s effects.

The biosimulation models use AI algorithms to detect patterns in past clinical trials and evaluate the relationships between the drug, patient demographic, and trial parameters. Researchers can use these models to test various aspects such as dosages, drug interactions, and effectiveness across demographics.

VeriSIM Life, for example, has its BIOiSIM™ platform that leverages AI and machine learning to simulate how individual organs and entire body systems will respond to various compounds.

Disease detection algorithms

Hospitals and medical research organizations are beginning to design disease-detection algorithms. These programs examine and analyze symptoms, medical histories, and procedures that would precede a diagnosis. The algorithm can then identify whether a patient is likely to develop a disease or is in the early stages of a medical condition.

Detection algorithms allow for more proactive care and treatments, which can either delay disease progression or limit symptoms. The algorithms will also allow CROs to recruit patients for prodromal or early-stage disease studies.

IQVIA has developed its own data-driven disease detection program that can analyze symptoms and patient features, then suggest a trial or refer the patient to a specialist.

Challenges When Using AI in Clinical Research

Of course, implementing new technologies comes with challenges and difficulties. This is especially true when it comes to a complex technology such as AI, which is still being developed and optimized.

Inconsistent data management

CROs face some difficulties when integrating AI technologies with their data capture and processing, especially on a global scale. Different institutions have their own protocols and SOPs (standard operating procedures) for collecting and organizing data. Without standardization, it becomes complicated to consolidate the data into a cohesive whole that an AI program can then analyze.

A unified global approach to data collection and processing would be the ideal, but implementing that would be an uphill battle. As it is, varying formats and quality levels pose a challenge for AI to parse the data and generate actionable insights.

Biases in databases

One particularly prominent challenge that AI faces is the inherent bias in research databases. Medical research and genetic databases are predominantly skewed toward Caucasian and European patients. There is a severe lack of diversity in medical studies, which tend to uphold able-bodied white persons as the “default.”

This means that when an AI algorithm is trained on these datasets, it has inadequate information about many demographics that are historically underrepresented but account for a significant portion of the population. That results in biased results that may not be accurate to these demographics, or in patient pools that exclude these demographics from being potential participants.

Accessibility and affordability

The emergence of new technologies generally comes with financial costs. Limited availability at the beginning of tech applications can mean the technology is more expensive to purchase or lease. It may also be costly to produce on a larger scale when it enters the equipment market.

Moreover, the technology may not be accessible to all or even a majority of CROs and medical institutions. Depending on where AI tech is produced and marketed, it may only be available to a limited group of organizations.

AI for CROs: Looking to the Future

Existing applications of artificial intelligence in clinical practices and trials have begun changing the way research is conducted and executed. From decentralization to biosimulation, CROs and medical research institutions have been leveraging AI to support, enhance, and transform their clinical research – all to the benefit of patients all over the world.

Vial is one such CRO that’s reimagining clinical trials in different medical fields through next-generation technology. We leverage our innovative platforms to execute clinical trials faster, better, and cheaper. Contact us today for a demo of our technologies!

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