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  - CHAP
A1  - Knoth, Alexander Henning
A1  - Kiy, Alexander
A1  - Müller, Ina
T1  - Das erste Semester von Studierenden der Wirtschafts- und Sozialwissenschaften im Spiegel der Reflect-App
T2  - DeLFI 2016 - Die 14. E-Learning Fachtagung Informatik 11.-14. September 2016 Potsdam
N2  - Mobile Applikationen eignen sich als strukturelle Unterstützungsangebote für Studierende während 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 Rückschlüsse sich hieraus auf die Problemlagen und Lernprozesse der Studierenden sowie für die  Studiengangsorganisation(en) ziehen lassen. Darüber hinaus werden die Stärken und Schwächen einer mobilen Applikation als sozial- und informationswissenschaftliches Amalgam zur strukturellen Unterstützung der Studieneingangsphase reflektiert.
KW  - Studieneinstieg
KW  - Studienorganisation
KW  - App
KW  - Reflexion
KW  - Professionalisierung
Y1  - 2016
UR  - http://subs.emis.de/LNI/Proceedings/Proceedings262/article13.html
SN  - 978-3-88579-656-5
IS  - P-262
SP  - 59
EP  - 70
PB  - Gesellschaft für Informatik e.V.
CY  - Bonn
ER  -