Common pitfalls and recommendations for grand challenges in medical artificial intelligence
With the impact of artificial intelligence (AI) algorithms on medical research on the rise, the importance of competitions for comparative validation of algorithms, so-called challenges, has been steadily increasing, to a point at which challenges can be considered major drivers of research, particu...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
11 June 2021
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| In: |
European urology focus
Year: 2021, Volume: 7, Issue: 4, Pages: 710-712 |
| ISSN: | 2405-4569 |
| DOI: | 10.1016/j.euf.2021.05.008 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.euf.2021.05.008 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S2405456921001607 
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| Author Notes: | Annika Reinke, Minu D. Tizabi, Matthias Eisenmann, Lena Maier-Hein |
| Summary: | With the impact of artificial intelligence (AI) algorithms on medical research on the rise, the importance of competitions for comparative validation of algorithms, so-called challenges, has been steadily increasing, to a point at which challenges can be considered major drivers of research, particularly in the biomedical image analysis domain. Given their importance, high quality, transparency, and interpretability of challenges is essential for good scientific practice and meaningful validation of AI algorithms, for instance towards clinical translation. This mini-review presents several issues related to the design, execution, and interpretation of challenges in the biomedical domain and provides best-practice recommendations. - Patient summary - This paper presents recommendations on how to reliably compare the usefulness of new artificial intelligence methods for analysis of medical images. |
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| Item Description: | Gesehen am 25.10.2021 |
| Physical Description: | Online Resource |
| ISSN: | 2405-4569 |
| DOI: | 10.1016/j.euf.2021.05.008 |