Topic modeling–a text-mining technique often used to uncover thematic structures in large collections of texts–has been increasingly frequently employed in the context of 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 fit a topic model, using the latent Dirichlet algorithm (K = 100). On that basis, we are able to interpret 91 content-based topics (representing distinct themes of interest in the field) and 6 ‘framing’ topics. On the basis of inter-topic correlations, we group the content-based topics into 8 clusters, thus providing a novel, fine-grained map of bioethics and philosophy of medicine. Finally, we conduct a number of diachronic analyses, examining how the ‘prominence’ of different topics changed across time. This way, we are able to identify distinct patterns in which bioethics and philosophy of medicine evolved and changed its focus throughout the past half a century. == Comments and suggestions welcome by early January ==