REAL-TIME CLINICAL TRIAL MONITORING WITH AI-POWERED ANALYTICS

Abstract

Clinical trials are essential to medical progress, but conventional methods of monitoring are antiquated, and are lagging and reactive, inefficiently requiring large amounts of resources, often into missing safety signals or deviations from the protocol in a timely manner. AI presents a practical answer to these historical inefficiencies, in a moment of unprecedented digital transformation. This article discusses the potential of bridging the new technologies developed for clinical trials with new methodologies and how AI-driven analytics can change the paradigm of how trials are monitored in the future by allowing real-time data integration, risk prediction and real-time monitoring of even complex, multicenter studies.

AI does not aim to substitute for humans but rather to enhance human decisions by analyzing incredibly large, complex, and varied datasets that include information from the electronic health record, wearable monitoring devices, and data provided directly by patients. Machine learning techniques may provide indications of anomalies, dropout prediction, and the development of new adverse events well in advance of traditional methods, enabling researchers to intervene proactively rather than reactively. Using case studies and examples of implementation we show the ways in which the future of AI has already begun to impact trials in the present.

We do but also problematize the concerns with adopting AI such as issues of data privacy, biases in algorithms, uncertainty in the law, and transparency in decision making. We must

ensure that these technologies remain our friends in ethical, effective research and don’t become a black box if they are to do that; to do this we need a humanized focus on patient’s safety and trust of the clinician and explainable AI.

Rather than fearing that technology will displace the human factor, through the combination of human intuition and technical capabilities, this paper advocates for a change of paradigm regarding the monitorization of clinical trials and the introduction of AI as a partner rather than a substitute. As industry continues to transition to decentralized and more adaptive trial designs, AI analyses will hopefully become more and more of the future of evidence-based medicine.