@incollection{KnothKiyMueller2016,
  author    = {Knoth, Alexander Henning and Kiy, Alexander and M{\"u}ller, Ina},
  title     = {Das erste Semester von Studierenden der Wirtschafts- und Sozialwissenschaften im Spiegel der Reflect-App},
  series = {DeLFI 2016 - Die 14. E-Learning Fachtagung Informatik 11.-14. September 2016 Potsdam},
  booktitle = {DeLFI 2016 - Die 14. E-Learning Fachtagung Informatik 11.-14. September 2016 Potsdam},
  number    = {P-262},
  publisher = {Gesellschaft f{\"u}r Informatik e.V.},
  address   = {Bonn},
  isbn      = {978-3-88579-656-5},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {59 -- 70},
  year      = {2016},
  abstract  = {Mobile Applikationen eignen sich als strukturelle Unterst{\"u}tzungsangebote f{\"u}r Studierende w{\"a}hrend des Studieneinstiegs. Durch die App Reflect.UP werden Studienorganisation, Studieninhalte und -ziele von Studierenden reflektiert. Der bewusste Umgang mit dem studentischen Kompetenzerwerb als wissenschaftliche eflexionskompetenz ist immanenter Bestandteil der akademischen Professionalisierung und steht in diesem Beitrag im Vordergrund. Gezeigt wird, wie aus Studienordnungen und Modulbeschreibungen systematisch Fragen zur studentischen Reflexion herausgearbeitet werden und dadurch ein Kompetenzraster entsteht. Die durch den praktischen Einsatz von Reflect.UP gewonnenen Daten werden ausgewertet und dahingehend diskutiert, welche R{\"u}ckschl{\"u}sse sich hieraus auf die Problemlagen und Lernprozesse der Studierenden sowie f{\"u}r die Studiengangsorganisation(en) ziehen lassen. Dar{\"u}ber hinaus werden die St{\"a}rken und Schw{\"a}chen einer mobilen Applikation als sozial- und informationswissenschaftliches Amalgam zur strukturellen Unterst{\"u}tzung der Studieneingangsphase reflektiert.},
  language  = {de}
}
@article{RigamontiEstelGehlenetal.2021,
  author    = {Rigamonti, Lia and Estel, Katharina and Gehlen, Tobias and Wolfarth, Bernd and Lawrence, James B. and Back, David A.},
  title     = {Use of artificial intelligence in sports medicine},
  series = {BMC Sports Science, Medicine \& Rehabilitation},
  volume    = {13},
  journal   = {BMC Sports Science, Medicine \& Rehabilitation},
  publisher = {BioMed Central},
  address   = {London},
  issn      = {2052-1847},
  doi       = {10.1186/s13102-021-00243-x},
  pages     = {17},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@misc{RigamontiEstelGehlenetal.2021,
  author    = {Rigamonti, Lia and Estel, Katharina and Gehlen, Tobias and Wolfarth, Bernd and Lawrence, James B. and Back, David A.},
  title     = {Use of artificial intelligence in sports medicine},
  series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  issn      = {1866-8364},
  doi       = {10.25932/publishup-51552},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-515528},
  pages     = {19},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}