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Fun and frustration
(2009)
This paper draws on Bernard Stiegler’s critique of “hyperindustrialism” to suggest that digital gaming is a privileged site for critiques of affective labor; games themselves routinely nod towards such critiques. Stiegler’s work adds, however, the important dimension of historical differentiation to recent critiques of affective labor, emphasizing “style” and “idiom” as key concerns in critical analyses of globalizing technocultures. These insights are applied to situate digital play in terms of affective labor, and conclude with a summary analysis of the gestural-technical stylistics of the Wii. The result is that interaction stylistics become comparable across an array of home networking devices, providing a gloss, in terms of affect, of the “simple enjoyment” Nintendo designers claim characterizes use of the Wii-console and its complex controllers.
Background
Artificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies.
Methods
Based on a literature review and clinical expert experience, five fictional “common” cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies.
Results
A mean of 25–36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor’s consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected.
Conclusions
All chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies.
Der zweite Band der DIGAREC Series beinhaltet Beiträge der DIGAREC Lectures 2008/09 sowie des Wissenschaftsforums der Deutschen Gamestage 2008 und 2009. Mit Beiträgen von Oliver Castendyk (Erich Pommer Institut), Stephan Günzel mit Michael Liebe und Dieter Mersch (Universität Potsdam), Andreas Lange (Computerspielemuseum Berlin), Ingrid Möller mit Barbara Krahé (Universität Potsdam), Klaus Spieler (Institut für digitale interaktive Kultur Berlin), James Tobias (University of California, Riverside), Stefan Böhme (HBK Braunschweig), Robert Glashüttner (Wien), Sven Jöckel (Universität Erfurt) mit Leyla Dogruel (FU Berlin), Michael Mosel (Universität Marburg), Sebastian Quack (HTW Berlin), Leif Rumbke (Hamburg) und Steffen P. Walz (ETH Zürich).