東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Auditing the Reasoning Processes of ...

DeGrave, Alex,

Linked to FindBook

Google Book

Amazon

博客來

Auditing the Reasoning Processes of Medical-Image AI /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Auditing the Reasoning Processes of Medical-Image AI // Alex DeGrave.
Author:	DeGrave, Alex,
Description:	1 electronic resource (94 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
Subject:	Medicine. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30993617
ISBN:	9798382212128

Auditing the Reasoning Processes of Medical-Image AI /
DeGrave, Alex,

Auditing the Reasoning Processes of Medical-Image AI /Alex DeGrave. - 1 electronic resource (94 pages)

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

While 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.

English

ISBN: 9798382212128Subjects--Topical Terms:

641104
Medicine.
Subjects--Index Terms:

Medical-images

Auditing the Reasoning Processes of Medical-Image AI /
LDR:02550nmm a22004573i 4500 001 2431214
005 20250603111406.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798382212128
035 $a (MiAaPQD)AAI30993617
035 $a AAI30993617
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a DeGrave, Alex, $e author. $3 3819913
245 1 0 $a Auditing the Reasoning Processes of Medical-Image AI / $c Alex DeGrave.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (94 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
500 $a Advisors: Lee, Su-In Committee members: Horwitz, Marshall; Daneshjou, Roxana; Althoff, Tim; Aravkin, Aleksandr.
502 $b Ph.D. $c University of Washington $d 2024.
520 $a While 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.
546 $a English
590 $a School code: 0250
650 4 $a Medicine. $3 641104
650 4 $a Medical imaging. $3 3172799
650 4 $a Computer science. $3 523869
650 4 $a Dermatology. $3 829009
653 $a Medical-images
653 $a Machine learning
653 $a Radiology
653 $a Clinical deployment
653 $a Reasoning processes
690 $a 0564
690 $a 0574
690 $a 0984
690 $a 0800
690 $a 0757
710 2 $a University of Washington. $b Computer Science and Engineering. $e degree granting institution. $3 3759111
720 1 $a Lee, Su-In $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-10B.
790 $a 0250
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30993617