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Auditing the Reasoning Processes of Medical-Image AI.
Auditing the Reasoning Processes of Medical-Image AI.
상세정보
- 자료유형
- 학위논문
- Control Number
- 0017160318
- International Standard Book Number
- 9798382212128
- Dewey Decimal Classification Number
- 610
- Main Entry-Personal Name
- DeGrave, Alex.
- Publication, Distribution, etc. (Imprint
- [S.l.] : University of Washington., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 94 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
- General Note
- Advisor: Lee, Su-In.
- Dissertation Note
- Thesis (Ph.D.)--University of Washington, 2024.
- Summary, Etc.
- 요약While medical artificial intelligence (AI) systems are achieving regulatory approval and clinical deployment across the world, the reasoning processes of these systems remain opaque to all stakeholders, including physicians, patients, regulators, and even the developers of these systems. Since the modern wave of medical AI relies on automatic learning of statistical patterns from large datasets-via 'machine-learning' techniques such as neural networks-they are prone to learning unexpected and potentially undesirable patterns, which may lead to pathological behavior in deployment. Here, we investigate the 'reasoning processes' of medical-image AI systems, that is, by forming a human-understandable, medically grounded conception of that mechanisms by which they generate predictions. Along the way, we develop new tools and frameworks as necessary to do so. Via these investigations, we uncover severe flaws in the reasoning of medical AI systems, and we build the first thorough, medically grounded picture of machine-learning-based medical-image AI reasoning processes.
- Subject Added Entry-Topical Term
- Medicine.
- Subject Added Entry-Topical Term
- Medical imaging.
- Subject Added Entry-Topical Term
- Computer science.
- Subject Added Entry-Topical Term
- Dermatology.
- Index Term-Uncontrolled
- Medical-images
- Index Term-Uncontrolled
- Machine learning
- Index Term-Uncontrolled
- Radiology
- Index Term-Uncontrolled
- Clinical deployment
- Index Term-Uncontrolled
- Reasoning processes
- Added Entry-Corporate Name
- University of Washington Computer Science and Engineering
- Host Item Entry
- Dissertations Abstracts International. 85-10B.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:655310
MARC
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■020 ▼a9798382212128
■035 ▼a(MiAaPQ)AAI30993617
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610
■1001 ▼aDeGrave, Alex.
■24510▼aAuditing the Reasoning Processes of Medical-Image AI.
■260 ▼a[S.l.]▼bUniversity of Washington. ▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a94 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-10, Section: B.
■500 ▼aAdvisor: Lee, Su-In.
■5021 ▼aThesis (Ph.D.)--University of Washington, 2024.
■520 ▼aWhile medical artificial intelligence (AI) systems are achieving regulatory approval and clinical deployment across the world, the reasoning processes of these systems remain opaque to all stakeholders, including physicians, patients, regulators, and even the developers of these systems. Since the modern wave of medical AI relies on automatic learning of statistical patterns from large datasets-via 'machine-learning' techniques such as neural networks-they are prone to learning unexpected and potentially undesirable patterns, which may lead to pathological behavior in deployment. Here, we investigate the 'reasoning processes' of medical-image AI systems, that is, by forming a human-understandable, medically grounded conception of that mechanisms by which they generate predictions. Along the way, we develop new tools and frameworks as necessary to do so. Via these investigations, we uncover severe flaws in the reasoning of medical AI systems, and we build the first thorough, medically grounded picture of machine-learning-based medical-image AI reasoning processes.
■590 ▼aSchool code: 0250.
■650 4▼aMedicine.
■650 4▼aMedical imaging.
■650 4▼aComputer science.
■650 4▼aDermatology.
■653 ▼aMedical-images
■653 ▼aMachine learning
■653 ▼aRadiology
■653 ▼aClinical deployment
■653 ▼aReasoning processes
■690 ▼a0564
■690 ▼a0574
■690 ▼a0984
■690 ▼a0800
■690 ▼a0757
■71020▼aUniversity of Washington▼bComputer Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g85-10B.
■790 ▼a0250
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160318▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
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