TY - JOUR A1 - Rigamonti, Lia A1 - Estel, Katharina A1 - Gehlen, Tobias A1 - Wolfarth, Bernd A1 - Lawrence, James B. A1 - Back, David A. T1 - Use of artificial intelligence in sports medicine BT - a report of 5 fictional cases JF - BMC Sports Science, Medicine & Rehabilitation N2 - 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. KW - Artificial intelligence KW - App KW - Sport medicine KW - Orthopedics KW - Pathologies Y1 - 2020 U6 - https://doi.org/10.1186/s13102-021-00243-x SN - 2052-1847 VL - 13 PB - BioMed Central CY - London ER - TY - JOUR A1 - Renz, André A1 - Vladova, Gergana T1 - Reinvigorating the discourse on Human-Centered artificial intelligence in educational technologies JF - Technology Innovation Management Review N2 - The increasing relevance of artificial intelligence (AI) applications in various domains has led to high expectations of benefits, ranging from precision, efficiency, and optimization to the completion of routine or time-consuming tasks. Particularly in the field of education, AI applications promise immense innovation potential. A central focus in this field is on analyzing and evaluating learner characteristics to derive learning profiles and create individualized learning environments. The development and implementation of such AI-driven approaches are related to learners' data, and thus involves several privacies, ethics, and morality challenges. In this paper, we introduce the concept of human-centered AI, and consider how an AI system can be developed in line with human values without posing risks to humanity. Because the education market is in the early stages of incorporating AI into educational tools, we believe that this is the right time to raise awareness about the use of principles that foster human-centered values and help in building responsible, ethical, and value-oriented AI. KW - Artificial intelligence KW - educational technology KW - intelligent tutoring systems KW - human-centered AI KW - design for value approach Y1 - 2021 U6 - https://doi.org/doi: 10.22215/timreview/1438 SN - 1927-0321 IS - 11 SP - 5 EP - 16 PB - Talent First Network CY - Ottawa ER -