256 Shades of gray: uncertainty and diagnostic error in radiology

Diagnosis ◽  
2017 ◽  
Vol 4 (3) ◽  
pp. 149-157 ◽  
Author(s):  
Michael A. Bruno

Abstract Radiologists practice in an environment of extraordinarily high uncertainty, which results partly from the high variability of the physical and technical aspects of imaging, partly from the inherent limitations in the diagnostic power of the various imaging modalities, and partly from the complex visual-perceptual and cognitive processes involved in image interpretation. This paper reviews the high level of uncertainty inherent to the process of radiological imaging and image interpretation vis-à-vis the issue of radiological interpretive error, in order to highlight the considerable degree of overlap that exists between these. The scope of radiological error, its many potential causes and various error-reduction strategies in radiology are also reviewed.

Diagnosis ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 215-225 ◽  
Author(s):  
Mindaugas Briedis ◽  
Ruta Briediene

AbstractThis paper uses novel qualitative research methods (phenomenology, ethnography and enactivism) to understand the cognitive processes through which radiologists interpret medical images to arrive at a diagnosis. From this perspective, diagnosis is not simply a matching of findings to retrieved mental images, but more properly an act of embodied or situated cognition, one that involves perception along with the actualization of professional memory and imagination and an expert-level understanding of the involved technology. Image interpretation involves a diverse set of factors, each of which is critical to arriving at the correct diagnostic interpretations, and conversely, may be the source of mis-interpretations and diagnostic error. Interpretation depends on the radiologist’s understanding of the imaging modality that was used, a deep appreciation of anatomy and comprehensive knowledge of relevant diseases and how they manifest in medical imaging. A range of personal and inter-personal factors may also come into play, including understanding the actions, values and goals of the patient, the imaging technicians and the clinicians and other medical professionals involved in the patient’s care. This multi-dimensional perspective provides novel insights regarding the cognitive aspects of diagnostic radiology and a novel framework for understanding how diagnostic errors arise in this process. Some of the findings of this research may have applications for diagnostic praxis in general, that is, beyond radiology diagnostics.


2008 ◽  
Vol 363 (1499) ◽  
pp. 2011-2019 ◽  
Author(s):  
Edwin Hutchins

Innate cognitive capacities are orchestrated by cultural practices to produce high-level cognitive processes. In human activities, examples of this phenomenon range from everyday inferences about space and time to the most sophisticated reasoning in scientific laboratories. A case is examined in which chimpanzees enter into cultural practices with humans (in experiments) in ways that appear to enable them to engage in symbol-mediated thought. Combining the cultural practices perspective with the theories of embodied cognition and enactment suggests that the chimpanzees' behaviour is actually mediated by non-symbolic representations. The possibility that non-human primates can engage in cultural practices that give them the appearance of symbol-mediated thought opens new avenues for thinking about the coevolution of human culture and human brains.


2021 ◽  
pp. 1-35
Author(s):  
Aaron R. Voelker ◽  
Peter Blouw ◽  
Xuan Choo ◽  
Nicole Sandra-Yaffa Dumont ◽  
Terrence C. Stewart ◽  
...  

Abstract While neural networks are highly effective at learning task-relevant representations from data, they typically do not learn representations with the kind of symbolic structure that is hypothesized to support high-level cognitive processes, nor do they naturally model such structures within problem domains that are continuous in space and time. To fill these gaps, this work exploits a method for defining vector representations that bind discrete (symbol-like) entities to points in continuous topological spaces in order to simulate and predict the behavior of a range of dynamical systems. These vector representations are spatial semantic pointers (SSPs), and we demonstrate that they can (1) be used to model dynamical systems involving multiple objects represented in a symbol-like manner and (2) be integrated with deep neural networks to predict the future of physical trajectories. These results help unify what have traditionally appeared to be disparate approaches in machine learning.


1998 ◽  
Vol 21 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Philippe G. Schyns ◽  
Robert L. Goldstone ◽  
Jean-Pierre Thibaut

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction.


2015 ◽  
Vol 22 (5) ◽  
pp. 632-639 ◽  
Author(s):  
Anouk van der Gijp ◽  
Cécile J. Ravesloot ◽  
Marieke F. van der Schaaf ◽  
Irene C. van der Schaaf ◽  
Josephine C.B.M. Huige ◽  
...  

2012 ◽  
Vol 52 (2) ◽  
pp. 678
Author(s):  
Steven McIntyre

Strategic and operational management in the exploration and production business is characterised by prediction and decision making in a data-rich, high-uncertainty environment. Analysis of predictive performance since the 1970s by multiple researchers indicates that predictions are subject to over-confidence and optimism negatively impacting performance. The situation is the same for other areas of human endeavour also operating within data-rich, high-uncertainty environments. Research in the fields of psychology and neuroscience indicates the way in which the human brain perceives, integrates and allocates significance to data is the cause. Significant effort has been dedicated to improving the quality of predictions. Many individual companies review their predictive performance during long periods, but few share their data or analysis with the industry at large. Data that is shared is generally presented at a high level, reducing transparency and making it difficult to link the analysis to the geology and data from which predictions are derived. This extended abstract presents an analysis of predictive performance from the Eromanga Basin where pre-drill predictions and detailed production data during a period of decades is available in the public domain, providing an opportunity to test the veracity of past observations and conclusions. Analysis of the dataset indicates that predictions made using both deterministic and probabilistic methodologies have been characterised by over-confidence and optimism. The reasons for this performance are discussed and suggestions for improving predictive capability provided.


2005 ◽  
Vol 11 (1-2) ◽  
pp. 233-244 ◽  
Author(s):  
Andy Clark

What do linguistic symbols do for minds like ours, and how (if at all) can basic embodied, dynamical, and situated approaches do justice to high-level human thought and reason? These two questions are best addressed together, since our answers to the first may inform the second. The key move in scaling up simple embodied cognitive science is, I argue, to take very seriously the potent role of human-built structures in transforming the spaces of human learning and reason. In particular, in this article I look at a range of cases involving what I dub surrogate situations. Here, we actively create restricted artificial environments that allow us to deploy basic perception-action-reason routines in the absence of their proper objects. Examples include the use of real-world models, diagrams, and other concrete external symbols to support dense looping interactions with a variety of stable external structures that stand in for the absent states of affairs. Language itself, I finally suggest, is the most potent and fundamental form of such surrogacy. Words are both cheap stand-ins for gross behavioral outcomes, and the concrete objects that structure new spaces for basic forms of learning and reason. A good hard look at surrogate situatedness thus turns the standard skeptical challenge on its head. But it raises important questions concerning what really matters about these new approaches, and it helps focus what I see as the major challenge for the future: how, in detail, to conceptualize the role of symbols (both internal and external) in dynamical cognitive processes.


2013 ◽  
Author(s):  
Richard P. Cooper With Contributi ◽  
Peter G. Yule ◽  
John Fox ◽  
David W. Glasspool ◽  
Richard P. Cooper

Stočarstvo ◽  
2021 ◽  
Vol 75 (1-2) ◽  
pp. 3-12
Author(s):  
Katarina Latin ◽  
Tajana Petrić ◽  
Boris Lukić ◽  
Željko Mahnet ◽  
Sven Menčik ◽  
...  

The Black Slavonian pig is an autochthonous pig breed in Croatia, which has recorded a continuous growth of the population followed by the higher number of breeders in recent years. The increase in population has removed the Black Slavonian breed from the category of endangered local breeds. The consequences of such a significant increase in population size in local breeds are often a high level of inbreeding, but also a high variability of the external traits of breeding individuals. Given that the main goal of the Breeding Program for Black Slavonian pigs is to preserve its phenotypic traits and breed-specific features, the paper presents the results of external traits analysis at 10 different points on the body, on a sample of 102 animals, aged between 10 and 24 months. Estimated mean values for wither’s height were 65 cm and 64 cm in boars and sows, respectively. Average body length was 128 cm for boars, and 126 cm for sows. The heart girth was 114 cm for both categories, while the height at the sacrum was 72 cm (boars), and 71 cm (sows). These results indicate very small or insignificant differences between male and female individuals, and refer that the body measurements of Black Slavonian pigs have not changed significantly in relation to its formation and development over time, as well as in relation to other local breeds from the neighbouring regions. With this in mind, selection work should be focused on control and preservation. Furthermore, the paper gives an overview of the population throughout history, as well as breeding practices.


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