Real-time data monitoring is crucial for ensuring patient safety during clinical trials, and AI is increasingly being used to enhance this process. In practice, AI can be deployed to monitor patient data continuously, flagging any signs of adverse events or protocol deviations as they occur. This allows for immediate intervention, which can prevent harm to participants and maintain the integrity of the trial.
For instance, AI systems can analyze data from wearable devices, such as heart rate monitors or glucose sensors, to detect early signs of adverse reactions. If a participant’s data indicates a potential issue, the system can alert the trial’s safety team, who can then take appropriate action, such as modifying the treatment or adjusting the dosage. This level of vigilance is particularly important in trials involving high-risk populations or experimental treatments.
To implement AI-driven real-time monitoring, sponsors should ensure that their AI tools are integrated with the trial’s electronic data capture (EDC) systems and are capable of handling large volumes of data from multiple sources. Additionally, clear protocols should be established for responding to AI-generated alerts, ensuring that safety teams can act swiftly to protect participants.
