相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI
Andreas Holzinger et al.
INFORMATION FUSION (2021)
On the ethics of algorithmic decision-making in healthcare
Thomas Grote et al.
JOURNAL OF MEDICAL ETHICS (2020)
Should Health Care Demand Interpretable Artificial Intelligence or Accept Black Box Medicine?
Fei Wang et al.
ANNALS OF INTERNAL MEDICINE (2020)
DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types
Adham Beykikhoshk et al.
BMC MEDICAL GENOMICS (2020)
A Framework for Advancing Precision Medicine in Clinical Trials for Mental Disorders
Eric J. Lenze et al.
JAMA PSYCHIATRY (2020)
Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome
Vuong Le et al.
BMC GENOMICS (2020)
Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review
Seyedeh Neelufar Payrovnaziri et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)
Black-box artificial intelligence: an epistemological and critical analysis
Manuel Carabantes
AI & SOCIETY (2020)
One Explanation Does Not Fit All The Promise of Interactive Explanations for Machine Learning Transparency
Kacper Sokol et al.
KUNSTLICHE INTELLIGENZ (2020)
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller
ARTIFICIAL INTELLIGENCE (2019)
Reporting of artificial intelligence prediction models
Gary S. Collins et al.
LANCET (2019)
Machine Learning in Medicine
Alvin Rajkomar et al.
NEW ENGLAND JOURNAL OF MEDICINE (2019)
Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed
Xiaoxuan Liu et al.
NATURE MEDICINE (2019)
Key challenges for delivering clinical impact with artificial intelligence
Christopher J. Kelly et al.
BMC MEDICINE (2019)
Definitions, methods, and applications in interpretable machine learning
W. James Murdoch et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Dissecting racial bias in an algorithm used to manage the health of populations
Ziad Obermeyer et al.
SCIENCE (2019)
Computer knows best? The need for value-flexibility in medical AI
Rosalind J. McDougall
JOURNAL OF MEDICAL ETHICS (2019)
A guide to deep learning in healthcare
Andre Esteva et al.
NATURE MEDICINE (2019)
High-performance medicine: the convergence of human and artificial intelligence
Eric J. Topol
NATURE MEDICINE (2019)
Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy
Rory Sayres et al.
OPHTHALMOLOGY (2019)
Artificial intelligence, bias and clinical safety
Robert Challen et al.
BMJ QUALITY & SAFETY (2019)
Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers
Dong Wook Kim et al.
KOREAN JOURNAL OF RADIOLOGY (2019)
A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence
Michael Haenlein et al.
CALIFORNIA MANAGEMENT REVIEW (2019)
Artificial intelligence and machine learning in clinical development: a translational perspective
Pratik Shah et al.
NPJ DIGITAL MEDICINE (2019)
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi et al.
IEEE ACCESS (2018)
Machine learning in medicine: Addressing ethical challenges
Effy Vayena et al.
PLOS MEDICINE (2018)
Unintended Consequences of Machine Learning in Medicine
Federico Cabitza et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)
European Union Regulations on Algorithmic Decision Making and a Right to Explanation
Bryce Goodman et al.
AI MAGAZINE (2017)
Facilitating Prospective Registration of Diagnostic Accuracy Studies: A STARD Initiative
Daniel A. Korevaar et al.
CLINICAL CHEMISTRY (2017)
The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts
Brent Daniel Mittelstadt et al.
SCIENCE AND ENGINEERING ETHICS (2016)
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)
How the machine 'thinks': Understanding opacity in machine learning algorithms
Jenna Burrell
BIG DATA & SOCIETY (2016)
An Agenda for Purely Confirmatory Research
Eric-Jan Wagenmakers et al.
PERSPECTIVES ON PSYCHOLOGICAL SCIENCE (2012)
The rise and fall of supervised machine learning techniques
Lars Juhl Jensen et al.
BIOINFORMATICS (2011)
Avoiding Another AI Winter
James Hendler
IEEE INTELLIGENT SYSTEMS (2008)
The enduring and evolving nature of the patient-physician relationship
D Roter
PATIENT EDUCATION AND COUNSELING (2000)