The three ghosts of medical AI: Can the black-box present deliver?

Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI

(2021) Andreas Holzinger et al.   Information Fusion

DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types

(2020) Adham Beykikhoshk et al.   BMC Medical Genomics

A Framework for Advancing Precision Medicine in Clinical Trials for Mental Disorders

(2020) Eric J. Lenze et al.   JAMA Psychiatry

Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome

(2020) Vuong Le et al.   BMC GENOMICS

Artificial intelligence, bias and clinical safety

(2019) Robert Challen et al.   BMJ Quality & Safety

Reporting of artificial intelligence prediction models

(2019) Gary S Collins et al.   LANCET

Machine Learning in Medicine

(2019) Alvin Rajkomar et al.   NEW ENGLAND JOURNAL OF MEDICINE

Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed

(2019)   NATURE MEDICINE

Key challenges for delivering clinical impact with artificial intelligence

(2019) Christopher J. Kelly et al.   BMC Medicine

On the ethics of algorithmic decision-making in healthcare

(2019) Thomas Grote et al.   JOURNAL OF MEDICAL ETHICS

Definitions, methods, and applications in interpretable machine learning

(2019) W. James Murdoch et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Dissecting racial bias in an algorithm used to manage the health of populations

(2019) Ziad Obermeyer et al.   SCIENCE

Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine?

(2019) Fei Wang et al.   ANNALS OF INTERNAL MEDICINE

Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)

(2018) Amina Adadi et al.   IEEE Access

Computer knows best? The need for value-flexibility in medical AI

(2018) Rosalind J McDougall   JOURNAL OF MEDICAL ETHICS

A guide to deep learning in healthcare

(2018) Andre Esteva et al.   NATURE MEDICINE

High-performance medicine: the convergence of human and artificial intelligence

(2018) Eric J. Topol   NATURE MEDICINE

Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy

(2018) Rory Sayres et al.   OPHTHALMOLOGY

Machine learning in medicine: Addressing ethical challenges

(2018) Effy Vayena et al.   PLOS MEDICINE

Explanation in artificial intelligence: Insights from the social sciences

(2018) Tim Miller   ARTIFICIAL INTELLIGENCE

Unintended Consequences of Machine Learning in Medicine

(2017) Federico Cabitza et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

(2016) Varun Gulshan et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Towards precision medicine

(2016) Euan A. Ashley   NATURE REVIEWS GENETICS

Deep learning

(2015) Yann LeCun et al.   NATURE

The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts

(2015) Brent Daniel Mittelstadt et al.   SCIENCE AND ENGINEERING ETHICS

The rise and fall of supervised machine learning techniques

(2011) L. J. Jensen et al.   BIOINFORMATICS

Avoiding Another AI Winter

(2008) James Hendler   IEEE INTELLIGENT SYSTEMS