Journal of evaluation in clinical practice
-
This paper examines the use of artificial intelligence (AI) for the diagnosis of autism spectrum disorder (ASD, hereafter autism). In so doing we examine some problems in existing diagnostic processes and criteria, including issues of bias and interpretation, and on concepts like the 'double empathy problem'. We then consider how novel applications of AI might contribute to these contexts. We're focussed specifically on adult diagnostic procedures as childhood diagnosis is already well covered in the literature.
-
According to an influential taxonomy of varieties of uncertainty in health care, existential uncertainty is a key aspect of uncertainty for patients. Although the term "existential uncertainty" appears across a number of disciplines in the research literature, its use is diffuse and inconsistent. To date there has not been a systematic attempt to define it. The aim of this study is to generate a theoretically-informed conceptualisation of existential uncertainty within the context of an established taxonomy. ⋯ Humans rely on identity, worldview, and a sense of meaning in life as ways of managing the ineradicable uncertainty of our being-in-the-world, and these can be challenged by a serious diagnosis. It is important that medical professionals acknowledge issues around existential uncertainty as well as issues around scientific uncertainty, and recognise when patients might be struggling with these. Further research is required to identify ways of measuring existential uncertainty and to develop appropriate interventions, but it is hoped that this conceptualisation provides a useful first step towards that goal.
-
Despite the great promises that artificial intelligence (AI) holds for health care, the uptake of such technologies into medical practice is slow. In this paper, we focus on the epistemological issues arising from the development and implementation of a class of AI for clinical practice, namely clinical decision support systems (CDSS). We will first provide an overview of the epistemic tasks of medical professionals, and then analyse which of these tasks can be supported by CDSS, while also explaining why some of them should remain the territory of human experts. ⋯ In practice, this means that the system indicates what factors contributed to arriving at an advice, allowing the user (clinician) to evaluate whether these factors are medically plausible and applicable to the patient. Finally, we defend that proper implementation of CRSS allows combining human and artificial intelligence into hybrid intelligence, were both perform clearly delineated and complementary empirical tasks. Whereas CRSSs can assist with statistical reasoning and finding patterns in complex data, it is the clinicians' task to interpret, integrate and contextualize.
-
The COVID-19 pandemic has transformed traditional in-person care into a new reality of virtual care for patients with complex chronic disease (CCD), but how has this transformation impacted clinical judgement? I argue that virtual specialist-patient interaction challenges clinical reasoning and clinical judgement (clinical reasoning combined with statistical reasoning). However, clinical reasoning can improve by recognising the abductive, deductive, and inductive methods that the clinician employs. Abductive reasoning leading to an inference to the best explanation or invention of an explanatory hypothesis is the default response to unfamiliar or confusing situations. ⋯ Clinical judgement in virtual encounters especially calls for Gestalt cognition to assess a situational pattern irreducible to its parts and independent of its particulars, so that efficient data interpretation and self-reflection are enabled. Gestalt cognition integrates abduction, deduction, and induction, appropriately divides the time and effort spent on each, and can compensate for reduced available information. Evaluating one's clinical judgement for those components especially vulnerable to compromise can help optimize the delivery of virtual care for patients with CCD.