Serviceeinschränkungen vom 12.-22.02.2026 - weitere Infos auf der UB-Homepage
American Psychological Association 6th edition

Stevens ER, Hartman J, Testa P, Mansukhani A, Monina C, Shunk A, Ranson D, Imberg Y, Cote A, Prabhu D, & Szerencsy A. (2025). Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study. JMIR Medical Informatics, 13, e73504-e73504. https://doi.org/10.2196/73504

ISO-690 (author-date, English)

STEVENS ER, HARTMAN J, TESTA P, MANSUKHANI A, MONINA C, SHUNK A, RANSON D, IMBERG Y, COTE A, PRABHU D und SZERENCSY A, 2025. Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study. JMIR medical informatics. 20 November 2025. Vol. 13, , p. e73504-e73504. DOI 10.2196/73504.

Modern Language Association 9th edition

Stevens ER, Hartman J, Testa P, Mansukhani A, Monina C, Shunk A, Ranson D, Imberg Y, Cote A, Prabhu D, und Szerencsy A. „Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study.“. JMIR Medical Informatics, Bd. 13, November 2025, S. e73504-e73504, https://doi.org/10.2196/73504.

Mohr Siebeck - Recht (Deutsch - Österreich)

Stevens ER/Hartman J/Testa P/Mansukhani A/Monina C/Shunk A u. a.: Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study., JMIR medical informatics 2025, e73504-e73504.

Emerald - Harvard

Stevens ER, Hartman J, Testa P, Mansukhani A, Monina C, Shunk A, Ranson D, Imberg Y, Cote A, Prabhu D und Szerencsy A. (2025), „Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study.“, JMIR Medical Informatics, Vol. 13, S. e73504-e73504.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.