Artificial intelligence (AI) is the theory and practice of designing computer systems to simulate actual processes of human intelligence. AI-powered systems rely on computers that embed machine learning (ML) to analyze large datasets and discover patterns across them. AI/ML thus provide powerful computing tools for pragmatic clinical trial (PCT) investigators. These tools support multimodal data analytics (e.g., data from electronic health records, wearables, and social media), advanced prediction, and large-scale modeling that far exceed the analytic capacities of many existing trial designs. Using AI/ML, a new class of digital PCTs has emerged (Inan et al 2020). It is anticipated that modern digital PCTs will increasingly serve as testbeds for AI/ML systems in clinical decision support (Yao et al 2021). Application of AI/ML could help find new ways to contain healthcare costs and facilitate longitudinal health surveillance. Among their strengths, AI/ML-enabled digital PCTs can help investigators to:

• Create structured data and metadata by standardizing data elements linked across PCT sites
• Interpret and iteratively learn from multimodal data inputs
• Passively monitor health status and behaviors using devices that transmit real-world data in real time, reducing time and costs for analysis
• Prospectively identify and digitally recruit eligible trial participants or PCT sites using pattern matching algorithms

However, AI/ML systems are only as accurate as the data on which they are trained. Multimodal linkages allow researchers to triangulate many sources of data that collectively improve how algorithms iteratively “learn” and begin to discover patterns indicative of health or hospital trends. Health-related data used for medical AI/ML research and development are often from various sources, including:

• Electronic health records
• Wearables and mobile health apps
• Social media

Both over- and under-representation of patient populations in these AI/ML training data sources can yield biased results that in turn harm real patients and exacerbate existing health inequities. For example, one study (Obermeyer et al 2019) found that Black patients were given a lower risk score than equally sick White patients based on data input that reflected that more health care dollars are spent on White versus Black patients. The algorithm misunderstood that signal to assume that meant that Black patients needed less healthcare, rather than had less access.

These and other technical limitations of AI/ML have ethical consequences that digital PCT investigators should anticipate and can proactively address at every stage in the research.