A manifesto on explainability for artificial intelligence in medicine

AI in Combating the COVID-19 Pandemic

(2022) Longbing Cao   IEEE INTELLIGENT SYSTEMS

Evaluating pointwise reliability of machine learning prediction

(2022) Giovanna Nicora et al.   JOURNAL OF BIOMEDICAL INFORMATICS

Relative explainability and double standards in medical decision-making

(2022) Hendrik Kempt et al.   Ethics and Information Technology

Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

(2021) Jianlong Zhou et al.   Electronics

CEFEs: A CNN Explainable Framework for ECG Signals

(2021) Barbara Mukami Maweu et al.   ARTIFICIAL INTELLIGENCE IN MEDICINE

Classification by ordinal sums of conjunctive and disjunctive functions for explainable AI and interpretable machine learning solutions

(2021) Miroslav Hudec et al.   KNOWLEDGE-BASED SYSTEMS

What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

(2021) Markus Langer et al.   ARTIFICIAL INTELLIGENCE

An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans

(2021) Matteo Pennisi et al.   ARTIFICIAL INTELLIGENCE IN MEDICINE

Machine Learning and XAI approaches for Allergy Diagnosis

(2021) Ramisetty Kavya et al.   Biomedical Signal Processing and Control

The Ten Commandments of Ethical Medical AI

(2021) Heimo Muller et al.   COMPUTER

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

(2021) Andreas Holzinger et al.   Information Fusion

Beware explanations from AI in health care

(2021) Boris Babic et al.   SCIENCE

Medical artificial intelligence

(2021) Karl Stöger et al.   COMMUNICATIONS OF THE ACM

Toward Human–AI Interfaces to Support Explainability and Causability in Medical AI

(2021) Andreas Holzinger et al.   COMPUTER

Human-centered XAI: Developing design patterns for explanations of clinical decision support systems

(2021) Tjeerd A.J. Schoonderwoerd et al.   INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES

Next-Generation Artificial Intelligence for Diagnosis

(2021) Julia Adler-Milstein et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Explainable AI meets persuasiveness: Translating reasoning results into behavioral change advice

(2020) Mauro Dragoni et al.   ARTIFICIAL INTELLIGENCE IN MEDICINE

A case-based ensemble learning system for explainable breast cancer recurrence prediction

(2020) Dongxiao Gu et al.   ARTIFICIAL INTELLIGENCE IN MEDICINE

Parental understanding of crucial medical jargon used in prenatal prematurity counseling

(2020) Nicole M. Rau et al.   BMC Medical Informatics and Decision Making

A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare

(2020) Amie J. Barda et al.   BMC Medical Informatics and Decision Making

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

(2020) Erico Tjoa et al.   IEEE Transactions on Neural Networks and Learning Systems

Evolution of resilience in protein interactomes across the tree of life

(2019) Marinka Zitnik et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

The seven tools of causal inference, with reflections on machine learning

(2019) Judea Pearl   COMMUNICATIONS OF THE ACM

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

(2019) Diego Ardila et al.   NATURE MEDICINE

In defense of the black box

(2019) Elizabeth A. Holm   SCIENCE

Methods for interpreting and understanding deep neural networks

(2018) Grégoire Montavon et al.   DIGITAL SIGNAL PROCESSING

A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

(2018) Jinying Chen et al.   JOURNAL OF MEDICAL INTERNET RESEARCH

An ontology-based interpretable fuzzy decision support system for diabetes diagnosis

(2018) Shaker El-Sappagh et al.   IEEE Access

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

(2018) Ryan Poplin et al.   Nature Biomedical Engineering

A Survey of Methods for Explaining Black Box Models

(2018) Riccardo Guidotti et al.   ACM COMPUTING SURVEYS

Explanation in artificial intelligence: Insights from the social sciences

(2018) Tim Miller   ARTIFICIAL INTELLIGENCE

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

Predictive data mining in clinical medicine: Current issues and guidelines

(2007) R BELLAZZI et al.   INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS