8 OCTOBER 2024According to the 21st Century Cures Act, the United States (US) Congress defined RWE as data regarding the usage, or the potential benefits or risks, of a drug derived from sources other than traditional clinical trials. The Food & Drug Administration (FDA) has elaborated further that "real-world data (RWD) are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources." Through analytics, "real-world evidence (RWE) is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD." Nowadays, artificial intelligence (AI), through machine learning (ML), deep learning (DL), digital innovation such as digital therapeutics (DTx), and decentralized clinical trials (DCTs) would be the enabler for the healthcare industry to provide optimal solutions to patients that are much faster, more precise and more efficient. AI PRIORITIES IN HEALTHCAREAlthough RWE can help understand patients, health conditions, and healthcare resource usage beyond randomized controlled trials (RCTs), its use in a regulatory capacity is in its infancy. Those who generate evidence and those who interpret and use it in a practical sense must keep in mind the limitations of the source data and analytical approaches used. Similar to data obtained from RCTs, transparency of methodology, use of best practices, conduct of handling protected health information, are essential when using RWD for the purpose of AI.Therefore, it is quite important to consider prioritizing through well-constructed frameworks, data quality and accessibility, and international collaborations. For regulatory purposes, in particular, early engagement with regulators will support subsequent efforts to obtain and analyze observational data. Finally, in an era of digital innovation, AI may enable extensive collection, aggregation, analyses, and interpretations to generate evidence and insights. By Kelly H. Zou, PhD, PStat®, FASA, Head, Global Medical Analytics, Real World Evidence, and Health Economics & Outcomes Research,ViatrisARTIFICIAL INTELLIGENCE AND THE HEALTHCARE INDUSTRYFurthermore, regarding the definition and applications of AI, the purpose of AI for drugs versus device regulations, the availability of quality data fed into explainable algorithms, as well as ethical conduct must be carefully considered. BARRIERS WHEN APPLYING AIPotential bottlenecks exist for RWE generation for value-added purposes, e.g., supporting regulatory submissions, label expansions, value-based contracts, comparative effectiveness Kelly H. ZouIN MY OPINIONIN MY OPINION
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