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Limits of trust in medical AI

Limits of trust in medical AI

20 March 2025
Joshua Hatherley
ArXivPDFHTML

Papers citing "Limits of trust in medical AI"

16 / 16 papers shown
Title
Federated learning, ethics, and the double black box problem in medical AI
Federated learning, ethics, and the double black box problem in medical AI
Joshua Hatherley
Anders Søgaard
Angela Ballantyne
Ruben Pauwels
FedML
58
0
0
29 Apr 2025
In defence of post-hoc explanations in medical AI
In defence of post-hoc explanations in medical AI
Joshua Hatherley
Lauritz Munch
Jens Christian Bjerring
32
0
0
29 Apr 2025
Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges
Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges
Joshua Hatherley
Robert Sparrow
37
11
0
11 Apr 2025
A moving target in AI-assisted decision-making: Dataset shift, model updating, and the problem of update opacity
A moving target in AI-assisted decision-making: Dataset shift, model updating, and the problem of update opacity
Joshua Hatherley
AAML
26
1
0
07 Apr 2025
Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?
Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?
Joshua Hatherley
34
1
0
31 Mar 2025
Evaluating Visual Explanations of Attention Maps for Transformer-based Medical Imaging
Minjae Chung
Jong Bum Won
Ganghyun Kim
Yujin Kim
Utku Ozbulak
MedIm
62
0
0
12 Mar 2025
The AI Double Standard: Humans Judge All AIs for the Actions of One
The AI Double Standard: Humans Judge All AIs for the Actions of One
Aikaterina Manoli
Janet V. T. Pauketat
Jacy Reese Anthis
90
2
0
08 Dec 2024
DWARF: Disease-weighted network for attention map refinement
DWARF: Disease-weighted network for attention map refinement
Haozhe Luo
Aurélie Pahud de Mortanges
Oana Inel
Abraham Bernstein
Mauricio Reyes
MedIm
33
3
0
24 Jun 2024
Foundation Model for Advancing Healthcare: Challenges, Opportunities,
  and Future Directions
Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
Yuting He
Fuxiang Huang
Xinrui Jiang
Yuxiang Nie
Minghao Wang
Jiguang Wang
Hao Chen
LM&MA
AI4CE
71
27
0
04 Apr 2024
Collaborative human-AI trust (CHAI-T): A process framework for active
  management of trust in human-AI collaboration
Collaborative human-AI trust (CHAI-T): A process framework for active management of trust in human-AI collaboration
Melanie Mcgrath
Andreas Duenser
Justine Lacey
Cecile Paris
20
4
0
02 Apr 2024
Trust in AI: Progress, Challenges, and Future Directions
Trust in AI: Progress, Challenges, and Future Directions
S. Afroogh
Ali Akbari
Evan Malone
Mohammadali Kargar
Hananeh Alambeigi
AI4TS
24
26
0
12 Mar 2024
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model
  Meta-AI (LLaMA) Using Medical Domain Knowledge
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge
Yunxiang Li
Zihan Li
Kai Zhang
Ruilong Dan
Steven Jiang
You Zhang
LM&MA
AI4MH
125
380
0
24 Mar 2023
Performance of a deep learning system for detection of referable
  diabetic retinopathy in real clinical settings
Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings
Verónica Sánchez-Gutiérrez
Paula Hernández-Martínez
F. Muñoz-Negrete
J. Engelberts
Allison M. Luger
M. V. Grinsven
13
3
0
11 May 2022
The Value of Measuring Trust in AI - A Socio-Technical System
  Perspective
The Value of Measuring Trust in AI - A Socio-Technical System Perspective
Michaela Benk
Suzanne Tolmeijer
F. Wangenheim
Andrea Ferrario
22
10
0
28 Apr 2022
Agree to Disagree: When Deep Learning Models With Identical
  Architectures Produce Distinct Explanations
Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations
Matthew Watson
Bashar Awwad Shiekh Hasan
Noura Al Moubayed
OOD
25
22
0
14 May 2021
Model Learning with Personalized Interpretability Estimation (ML-PIE)
Model Learning with Personalized Interpretability Estimation (ML-PIE)
M. Virgolin
A. D. Lorenzo
Francesca Randone
Eric Medvet
M. Wahde
24
29
0
13 Apr 2021
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