Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues
Giuseppe Pasculli, Marco Virgolin, Puja Myles, Anna A. Vidovszky, Charles K. Fisher, Elisabetta Biasin, Miranda Mourby, Francesco Pappalardo, Saverio D’Amico, Mario Torchia, Alexander Chebykin, Vincenzo Carbone, Luca Emili, Daniel Roeshammar
Abstract
With the recent and evolving regulatory frameworks regarding the usage of Artificial Intelligence (AI) in both drug and medical device development, the differentiation between data derived from observed ('true' or 'real') sources and artificial data obtained using process-driven and/or (data-driven) algorithmic processes is emerging as a critical consideration in clinical research and regulatory discourse. We conducted a critical literature review that revealed evidence of the current ambivalent usage of the term "synthetic" (along with derivative terms) to refer to "true/observed" data in the context of clinical trials and AI-generated data (or "artificial" data). This paper, stemming from a critical evaluation of different perspectives captured from the scientific literature and recent regulatory endeavors, seeks to elucidate this distinction, exploring their respective utilities, regulatory stances, and upcoming needs, as well as the potential for both data types in advancing medical science and therapeutic development.