Speaker
Description
Artificial intelligence (AI) is intended to support clinicians, therapists, patients, hospital managers, and clinical data scientists at all levels. This includes, for example, making clinical diagnoses, understanding the causes of diseases, and planning clinical studies. The enormous increase in the importance of AI in medicine has led to the development of several guidelines (e.g., CONSORT-AI, TRIPOD-AI, and SPIRIT-AI), which, in addition to general aspects of dealing with AI, also cover computer-aided and intelligent decision support systems. In our work, we aim to explain how biostatistical methodology can contribute (and is already contributing) to advancing these developments in a science-driven manner, thereby enhancing the trustworthiness of AI in medicine. In particular, we address current biostatistical topics that have great potential for AI research and application. These include, for example, (re)sampling and study designs in the generation and interpretation of AI-based analysis results, classical and modern sample size planning for AI-supported scientific studies, aspects of the validation and reproducibility of AI methods and their outputs, suitable data infrastructures, the quantification of uncertainty in AI-based analysis results, and a discussion of estimands in the context of AI-based knowledge gain.
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