[Adobe Stock]
As machine learning and deep learning technologies advance thanks to advances in computation, algorithms and data availability, the possibilities of the technology continue to expand in medicine. While these AI-driven approaches have real potential, such systems demand large volumes of representative data, careful privacy and security scrutiny and thoughtful long-term strategic planning. In this Q&A, Kathryn Rough, associate director of the Center for Advanced Evidence Generation at IQVIA, discusses the impact of deep learning on healthcare delivery and recommends steps to take during the design, training, evaluation and deployment phases to increase the likelihood that these models will be safe, effective and ethical when trained on real-world health data. Rough also explores the role of epidemiologists in evaluating these technologies as part of multidisciplinary teams and provides advice for health…Pharmacovigilance in personalized medicine: Adapting to a new era of drug development
[Image courtesy of LeoWolfert via iStock]
One of the ways that the COVID-19 pandemic had a profound impact on the pharma industry was how it highlighted the importance of pharmacovigilance. As personalized medicine gained traction, drug developers had to evolve their strategies to ensure safety and efficacy. In their report, “The Future of Pharmacovigilance,” Dr. Sabine Jeck-Thole and Ben Enejo, partners at Arthur D. Little, discussed the challenges and opportunities in this new landscape. Embracing the shift in perception of adverse events and side effects“When we heard about adverse events and side effects, it was not bad news,” Jeck-Thole said in an interview. “It indicated that we understood our drug or vaccine much better than without any safety information.” She emphasized that this shift in perception was essential for the industry as it moved towards pharmacovigilance in personal…