Journal of evaluation in clinical practice
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This paper explores the possibility of AI-based addendum therapy for borderline personality disorder, its potential advantages and limitations. Identity disturbance in this condition is strongly connected to self-narratives, which manifest excessive incoherence, causal gaps, dysfunctional beliefs, and diminished self-attributions of agency. ⋯ The suggestion of this paper is that human-to-human therapy could be complemented by AI assistance holding out the promise of making patients' self-narratives more coherent through improving the accuracy of their self-assessments, reflection on their emotions, and understanding their relationships with others. Theoretical and pragmatic arguments are presented in favour of this idea, and certain technical solutions are suggested to implement it.
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Parts 1 and 2 in this series of three articles have shown that and how strong evidence-based medicine has neither a coherent theoretical foundation nor creditable application to clinical practice. Because of its core commitment to the discredited positivist tradition it holds both a false concept of scientific knowledge and misunderstandings concerning clinical decision-making. Strong EBM continues attempts to use flawed adjustments to recover from the unsalvageable base view. ⋯ While most of papers 1, 2, and 3 are written in the classical mode of contrasting the theoretical-logical and empirical evidence offered by contending positions bearing on the decision making and judgement in clinical practice, a shift occurs when considerations move beyond what is possible for clinical practitioners to accomplish. A different, discontinuous level of power operates in the trans-personal realm of instrumental policy, insurance, and hospital management practices. In this social-economic-political-ethical realm what happens in clinical practice today increasingly becomes a matter of what is "done unto" clinical practitioners, of what hampers their professional action and thus care of individual patients and clients.
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One of the sectors challenged by the COVID-19 pandemic is medical research. COVID-19 originates from a novel coronavirus (SARS-CoV-2) and the scientific community is faced with the daunting task of creating a novel model for this pandemic or, in other words, creating novel science. This paper is the first part of a series of two papers that explore the intricate relationship between the different challenges that have hindered biomedical research and the generation of scientific knowledge during the COVID-19 pandemic. ⋯ The COVID-19 pandemic presented challenges in terms of (1) finding and prioritising relevant research questions and (2) choosing study designs that are appropriate for a time of emergency.
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The goals of learning health systems (LHS) and of AI in medicine overlap in many respects. Both require significant improvements in data sharing and IT infrastructure, aim to provide more personalized care for patients, and strive to break down traditional barriers between research and care. However, the defining features of LHS and AI diverge when it comes to the people involved in medicine, both patients and providers. ⋯ LHS also encourage better coordination of specialists across the health system, but AI aims to replace many specialists with technology and algorithms. This paper argues that these points of conflict may require a reconsideration of the role of humans in medical decision making. Although it is currently unclear to what extent machines will replace humans in healthcare, the parallel development of LHS and AI raises important questions about the exact role for humans within AI-enabled healthcare.