This chapter examines the shifting landscape of specialization in light of AI and automation, contrasting historical and modern roles. It discusses the integration of AI in statistics, from automating tasks to improving data analysis, while addressing challenges like biases and trust in AI outputs. Emphasizing the necessity of human expertise, the chapter highlights the evolving responsibilities of statisticians in a technology-driven environment.
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Webinar with Yannis Jemiai, Flaminia Chiesa, and Benjamin Piske
β Learn how statisticians can step into leadership roles as data science becomes more strategic.
β Understand the impact of AI, real-world evidence, and decentralized trials on clinical trial design.
β Hear practical advice for staying relevant in the age of reimagined RCTs.
β Get insight into building confidence as a statistician when working across disciplines.
π Medical Data Leaders Community β Join my network of statisticians and data leaders to enhance your influencing skills.
π My New Book: How to Be an Effective Statistician - Volume 1β Itβs packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.
If youβre working on evidence generation plans or preparing for joint clinical advice, this episode is packed with insights you donβt want to miss.
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