AI for Doctors—A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging
Successful adoption of artificial intelligence (AI) in medical imaging requires medical professionals to understand underlying principles and techniques. However, educational offerings tailored to the need of medical professionals are scarce. To fill this gap, we created the course “AI for Doctors:...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/9/10/1278 |
| _version_ | 1797514448332652544 |
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| author | Dennis M. Hedderich Matthias Keicher Benedikt Wiestler Martin J. Gruber Hendrik Burwinkel Florian Hinterwimmer Tobias Czempiel Judith E. Spiro Daniel Pinto dos Santos Dominik Heim Claus Zimmer Daniel Rückert Jan S. Kirschke Nassir Navab |
| author_facet | Dennis M. Hedderich Matthias Keicher Benedikt Wiestler Martin J. Gruber Hendrik Burwinkel Florian Hinterwimmer Tobias Czempiel Judith E. Spiro Daniel Pinto dos Santos Dominik Heim Claus Zimmer Daniel Rückert Jan S. Kirschke Nassir Navab |
| author_sort | Dennis M. Hedderich |
| collection | DOAJ |
| description | Successful adoption of artificial intelligence (AI) in medical imaging requires medical professionals to understand underlying principles and techniques. However, educational offerings tailored to the need of medical professionals are scarce. To fill this gap, we created the course “AI for Doctors: Medical Imaging”. An analysis of participants’ opinions on AI and self-perceived skills rated on a five-point Likert scale was conducted before and after the course. The participants’ attitude towards AI in medical imaging was very optimistic before and after the course. However, deeper knowledge of AI and the process for validating and deploying it resulted in significantly less overoptimism with respect to perceivable patient benefits through AI (<i>p</i> = 0.020). Self-assessed skill ratings significantly improved after the course, and the appreciation of the course content was very positive. However, we observed a substantial drop-out rate, mostly attributed to the lack of time of medical professionals. There is a high demand for educational offerings regarding AI in medical imaging among medical professionals, and better education may lead to a more realistic appreciation of clinical adoption. However, time constraints imposed by a busy clinical schedule need to be taken into account for successful education of medical professionals. |
| first_indexed | 2024-03-10T06:31:46Z |
| format | Article |
| id | doaj.art-711a391bcb824cc9853660dcebc89853 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T06:31:46Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-711a391bcb824cc9853660dcebc898532023-11-22T18:24:29ZengMDPI AGHealthcare2227-90322021-09-01910127810.3390/healthcare9101278AI for Doctors—A Course to Educate Medical Professionals in Artificial Intelligence for Medical ImagingDennis M. Hedderich0Matthias Keicher1Benedikt Wiestler2Martin J. Gruber3Hendrik Burwinkel4Florian Hinterwimmer5Tobias Czempiel6Judith E. Spiro7Daniel Pinto dos Santos8Dominik Heim9Claus Zimmer10Daniel Rückert11Jan S. Kirschke12Nassir Navab13Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, D-81675 Munich, GermanyComputer Aided Medical Procedures, Technical University of Munich, D-81675 Munich, GermanyDepartment of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, D-81675 Munich, GermanyDepartment of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, D-81675 Munich, GermanyComputer Aided Medical Procedures, Technical University of Munich, D-81675 Munich, GermanyComputer Aided Medical Procedures, Technical University of Munich, D-81675 Munich, GermanyComputer Aided Medical Procedures, Technical University of Munich, D-81675 Munich, GermanyDepartment of Radiology, University Hospital, LMU Munich, D-80336 Munich, GermanyDepartment of Radiology, University Hospital Cologne, D-50937 Cologne, GermanyComputer Aided Medical Procedures, Technical University of Munich, D-81675 Munich, GermanyDepartment of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, D-81675 Munich, GermanyInstitute for Artificial Intelligence and Informatics in Medicine, Technical University of Munich, D-81675 Munich, GermanyDepartment of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, D-81675 Munich, GermanyComputer Aided Medical Procedures, Technical University of Munich, D-81675 Munich, GermanySuccessful adoption of artificial intelligence (AI) in medical imaging requires medical professionals to understand underlying principles and techniques. However, educational offerings tailored to the need of medical professionals are scarce. To fill this gap, we created the course “AI for Doctors: Medical Imaging”. An analysis of participants’ opinions on AI and self-perceived skills rated on a five-point Likert scale was conducted before and after the course. The participants’ attitude towards AI in medical imaging was very optimistic before and after the course. However, deeper knowledge of AI and the process for validating and deploying it resulted in significantly less overoptimism with respect to perceivable patient benefits through AI (<i>p</i> = 0.020). Self-assessed skill ratings significantly improved after the course, and the appreciation of the course content was very positive. However, we observed a substantial drop-out rate, mostly attributed to the lack of time of medical professionals. There is a high demand for educational offerings regarding AI in medical imaging among medical professionals, and better education may lead to a more realistic appreciation of clinical adoption. However, time constraints imposed by a busy clinical schedule need to be taken into account for successful education of medical professionals.https://www.mdpi.com/2227-9032/9/10/1278artificial intelligencemedical imagingmachine learningclinical translationcontinuing medical education |
| spellingShingle | Dennis M. Hedderich Matthias Keicher Benedikt Wiestler Martin J. Gruber Hendrik Burwinkel Florian Hinterwimmer Tobias Czempiel Judith E. Spiro Daniel Pinto dos Santos Dominik Heim Claus Zimmer Daniel Rückert Jan S. Kirschke Nassir Navab AI for Doctors—A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging Healthcare artificial intelligence medical imaging machine learning clinical translation continuing medical education |
| title | AI for Doctors—A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging |
| title_full | AI for Doctors—A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging |
| title_fullStr | AI for Doctors—A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging |
| title_full_unstemmed | AI for Doctors—A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging |
| title_short | AI for Doctors—A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging |
| title_sort | ai for doctors a course to educate medical professionals in artificial intelligence for medical imaging |
| topic | artificial intelligence medical imaging machine learning clinical translation continuing medical education |
| url | https://www.mdpi.com/2227-9032/9/10/1278 |
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