This article is about nursing theories, the development of nursing knowledge and the underlying, hidden epistemology. The current technical–economical rationality in society and health care calls for a specific kind of knowledge based on a traditional Western, Socratic view of science. This has an immense influence on the development of nursing knowledge. The purpose of the article was therefore to discuss the hidden epistemology of nursing knowledge and theories seen in a broad historical context and point to an alternative epistemology (...) for a future context. It is a question about which nursing theories and what nursing knowledge should be developed in order to benefit patients and relatives of the future. We suggest that future knowledge development in nursing be developed in an interchange between theory and practice and guided by philosophy like a kind of pendulum where all three elements are treated as equals. We suggest a framework for the development of nursing knowledge based on a caring‐ethical practice, a theory on life phenomena in suffering and relationship‐based nursing, and thereby, we may be able to help patients to be cured, to recover, to be alleviated or comforted when suffering. (shrink)

The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow movement towards improved understanding of pathophysiology and treatments compared with more common illnesses. The push towards evidence-based medicine (EBM), which prioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the methodologies endorsed by EBM, due to a number of constraints. While other trial designs have been suggested to overcome (...) these limitations (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data. This paper critically examines the pitfalls of EBM (and its trial design offshoots) as it pertains to rare diseases, exploring the current landscape of AI as a potential solution to these challenges. This discussion is also taken a step further, providing philosophical commentary on the weaknesses and dangers of AI algorithms applied to rare disease research. While not proposing a singular solution, this article does provide a thoughtful reminder that no ‘one-size-fits-all’ approach exists in the complex world of rare diseases. We must balance cautious optimism with critical evaluation of new research paradigms and technology, while at the same time not neglecting the ever-important aspect of patient values and preferences, which may be challenging to incorporate into computer-driven models. (shrink)