Topic modeling-a text-mining technique often used to uncover thematic structures in large collections of texts-has been increasingly frequently used in the context of the analysis of scholarly output. In this study, we construct a corpus of 19,488 texts published since 1971 in seven leading journals in the field of bioethics and philosophy of medicine, and we use a machine learning algorithm to identify almost 100 topics representing distinct themes of interest in the field. On the basis of intertopic correlations, we group the content-based topics into eight clusters, thus providing a novel, fine-grained intellectual map of bioethics and philosophy of medicine. Moreover, we conduct a number of diachronic analyses, examining how the "prominence" of different topics has changed across time. In this way, we are able to observe the distinct patterns in which bioethics and philosophy of medicine have evolved and changed their focus over the past half a century.

The authors declare no conflict of interest.

• 805498/ERC_/European Research Council/International
• 805498/European Research Council (ERC) under the H2020 European Research Council Research and Innovation Program