Bayesian brain theory: Computational neuroscience of belief.

Bayesian brain theory, a computational framework grounded in the principles of Predictive Processing (PP), proposes a mechanistic account of how beliefs are formed and updated. This theory assumes that the brain encodes a generative model of its environment, made up of probabilistic beliefs organized in networks, from which it generates predictions about future sensory inputs. The difference between predictions and sensory signals produces prediction errors, which are used to update belief networks.

Exploring the interplay of clinical reasoning and artificial intelligence in psychiatry: Current insights and future directions.

For many years, it has been widely accepted in the psychiatric field that clinical practice cannot be reduced to finely tuned statistical prediction systems utilizing diverse clinical data. Clinicians are recognized for their unique and irreplaceable roles. In this brief historical overview, viewed through the lens of artificial intelligence (AI), we propose that comprehending the reasoning behind AI can enhance our understanding of clinical reasoning.

Clues for improvement of research in objective structured clinical examination.

While objective clinical structured examination (OSCE) is a worldwide recognized and effective method to assess clinical skills of undergraduate medical students, the latest Ottawa conference on the assessment of competences raised vigorous debates regarding the future and innovations of OSCE. This study aimed to provide a comprehensive view of the global research activity on OSCE over the past decades and to identify clues for its improvement. We performed a bibliometric and scientometric analysis of OSCE papers published until March 2024.