Common Pitfalls and Recommendations for Grand Challenges in Medical Artificial Intelligence.

European Urology Focus
Annika ReinkeLena Maier-Hein

Abstract

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.

References

Sep 9, 2018·European Radiology·Leo JoskowiczJ Sosna
Dec 13, 2018·Nature Communications·Lena Maier-HeinAnnette Kopp-Schneider
Jul 24, 2020·Nature Communications·Daniel C CastroBen Glocker
Jan 29, 2021·Scientific Reports·Manuel WiesenfarthAnnette Kopp-Schneider

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