Summary of Data Set Terminology Of Deep Learning in Medicine: a Historical Review and Recommendation, by Shannon L. Walston et al.
Data Set Terminology of Deep Learning in Medicine: A Historical Review and Recommendation
by Shannon L. Walston, Hiroshi Seki, Hirotaka Takita, Yasuhito Mitsuyama, Shingo Sato, Akifumi Hagiwara, Rintaro Ito, Shouhei Hanaoka, Yukio Miki, Daiju Ueda
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a narrative review that aims to clarify the terminology used in medicine and deep learning-based artificial intelligence (AI) engineering. The review provides historical context for the terms used in medical AI contexts, emphasizing the importance of clarity when these terms are used. It also offers solutions to mitigate misunderstandings by readers from either field. Specifically, the paper examines the evolution of terms for data sets, including random splitting, cross-validation, temporal, geographic, internal, and external sets. The accurate and standardized description of these data sets is crucial for demonstrating the robustness and generalizability of AI applications in medicine. The review also identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps medical professionals and AI engineers understand each other better by explaining confusing terms used in AI research. The authors look at how terms like “validation” and data set splitting have changed over time. They show that using standardized terms, like “training set,” “validation (or tuning) set,” and “test set,” can help prevent misunderstandings. This is important for making sure AI applications are reliable and work well in different settings. |
Keywords
» Artificial intelligence » Deep learning
