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ASSESSING METHODS AND TOOLS TO IMPROVE REPORTING, INCREASE TRANSPARENCY, AND REDUCE FAILURES IN MACHINE LEARNING APPLICATIONS IN HEALTHCARE
| Title: | ASSESSING METHODS AND TOOLS TO IMPROVE REPORTING, INCREASE TRANSPARENCY, AND REDUCE FAILURES IN MACHINE LEARNING APPLICATIONS IN HEALTHCARE. |
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| Name(s): |
Garbin, Christian, author Marques, Oge, Thesis advisor Florida Atlantic University, Degree grantor Department of Computer and Electrical Engineering and Computer Science College of Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Date Created: | 2020 | |
| Date Issued: | 2020 | |
| Publisher: | Florida Atlantic University | |
| Place of Publication: | Boca Raton, Fla. | |
| Physical Form: | application/pdf | |
| Extent: | 114 p. | |
| Language(s): | English | |
| Summary: | Artificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The software engineering community has accumulated a large body of knowledge over the decades on how to develop, release, and maintain products. AI products, being software products, benefit from some of that accumulated knowledge, but not all of it. AI products diverge from traditional software products in fundamental ways: their main component is not a specific piece of code, written for a specific purpose, but a generic piece of code, a model, customized by a training process driven by hyperparameters and a dataset. Datasets are usually large and models are opaque. We cannot directly inspect them as we can inspect the code of traditional software products. We need other methods to detect failures in AI products. | |
| Identifier: | FA00013580 (IID) | |
| Degree granted: | Thesis (M.S.)--Florida Atlantic University, 2020. | |
| Collection: | FAU Electronic Theses and Dissertations Collection | |
| Note(s): | Includes bibliography. | |
| Subject(s): |
Machine learning Artificial intelligence Healthcare |
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| Held by: | Florida Atlantic University Libraries | |
| Sublocation: | Digital Library | |
| Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00013580 | |
| Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU | |
| Is Part of Series: | Florida Atlantic University Digital Library Collections. |
