Research graph
References from Responsible AI in healthcare: Navigating ethics and governance challenges. Local targets link to admitted publications; unresolved targets remain external evidence.
Explainability for artificial intelligence in healthcare
10.1186/s12911-020-01332-6 · 2020 · External reference
Transforming health policy through AI
2018 · External reference
Principled ethical AI in healthcare systems
2022 · External reference
Big data’s disparate impact
2016 · External reference
Big data and machine learning in health care
10.1001/jama.2017.18391 · 2018 · External reference
Implementing machine learning in health care—Addressing ethical challenges
10.1056/nejmp1714229 · 2018 · External reference
Machine learning and prediction in medicine
10.1056/nejmp1702071 · 2017 · External reference
The fate of medicine in the time of AI
10.1016/s0140-6736(18)31925-1 · 2018 · External reference
The measure and mismeasure of fairness
2018 · External reference
Algorithmic bias in autonomous systems
2017 · External reference
The potential for artificial intelligence in healthcare
10.7861/futurehosp.6-2-94 · 2019 · External reference
Repairing innovation: a study of integrating AI in clinical care
2020 · External reference
Artificial intelligence in health care: will the value match the hype?
10.1001/jama.2019.4914 · 2019 · External reference
Dermatologist-level classification of skin cancer with deep neural networks
10.1038/nature21056 · 2017 · External reference
A guide to deep learning in healthcare
10.1038/s41591-018-0316-z · 2019 · External reference
Unresolved reference
2019 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
2022 · External reference
The clinician and dataset shift in artificial intelligence
2019 · External reference
Delivering CBT using conversational agents
10.2196/mental.7785 · 2017 · External reference
Translating principles into practices of digital ethics
2019 · External reference
A unified framework of five principles for AI in society
2019 · External reference
Bias in computer systems
10.1145/230538.230561 · 1996 · External reference
Datasheets for datasets
10.1145/3458723 · 2021 · External reference
Ethical and legal challenges of AI-driven healthcare
2020 · External reference
European Union regulations on algorithmic decision-making and a “right to explanation”
2017 · External reference
Planning and evaluating remote consultation services
2020 · External reference
Beyond adoption: a new framework for theorizing and evaluating nonadoption of health technologies
10.2196/jmir.8775 · 2017 · External reference
On the ethics of algorithmic decision-making in healthcare
10.1136/medethics-2019-105586 · 2020 · External reference
Practical implementation of AI technologies in medicine
10.1038/s41591-018-0307-0 · 2019 · External reference
What do we need to build explainable AI systems for the medical domain?
2017 · External reference
Unresolved reference
2019 · External reference
How machine-learning recommendations influence clinician treatment selections
2021 · External reference
The global landscape of AI ethics guidelines
10.1038/s42256-019-0088-2 · 2019 · External reference
Machine learning in medicine: where are we now?
2020 · External reference
Key challenges for delivering clinical impact with AI
10.1186/s12916-019-1426-2 · 2019 · External reference
Promoting innovation in healthcare through clinical AI
2017 · External reference
AI in medicine must be explainable
10.1038/s41591-021-01461-z · 2021 · External reference
Mammographic breast density assessment using AI
10.1148/radiol.2018180694 · 2019 · External reference
Unresolved reference
2019 · External reference
The mythos of model interpretability
10.1145/3233231 · 2018 · External reference
Artificial intelligence and black-box medical decisions: accuracy versus explainability
10.1002/hast.973 · 2019 · External reference
Against pandemic research exceptionalism
10.1126/science.abc1731 · 2020 · External reference
Ethical foundations of clinical decision-making with artificial intelligence
2022 · External reference
A unified approach to interpreting model predictions
2017 · External reference
An introduction to artificial intelligence in behavioral and mental health care
2014 · External reference
Ethical issues and artificial intelligence technologies in behavioral health
2016 · External reference
What do clinicians want? Factors influencing AI adoption in healthcare
2020 · External reference
Clinical research and ethical integration of healthcare AI
10.1038/s41591-020-1035-9 · 2020 · External reference
Reproducibility in machine learning for health research
10.1126/scitranslmed.abb1655 · 2021 · External reference
international evaluation of an AI system for breast cancer screening
10.1038/s41586-019-1799-6 · 2020 · External reference
Explanation in artificial intelligence: insights from the social sciences
10.1016/j.artint.2018.07.007 · 2019 · External reference
Model cards for model reporting
2019 · External reference
Ethics of artificial intelligence in medicine and healthcare
2021 · External reference
From what to how: an initial review of AI ethics tools and methods
10.1007/s11948-019-00165-5 · 2020 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
2018 · External reference
Dissecting racial bias in an algorithm used to manage the health of populations
10.1126/science.aax2342 · 2019 · External reference
Unresolved reference
2019 · External reference
The “inconvenient truth” about AI in healthcare
10.1038/s41746-019-0155-4 · 2019 · External reference
Regulation of predictive analytics in medicine
10.1126/science.aaw0029 · 2019 · External reference
Black-box medicine
2017 · External reference
Ensuring fairness in machine learning to advance health equity
10.7326/m18-1990 · 2018 · External reference
AI in medicine
2022 · External reference
Artificial intelligence-enabled healthcare delivery
10.1177/0141076818815510 · 2019 · External reference
“Why should I trust you?” Explaining the predictions of any classifier
2016 · External reference
Stop explaining black box machine learning models for high-stakes decisions and use interpretable models instead
10.1038/s42256-019-0048-x · 2019 · External reference
Clinician checklist for assessing AI applications in healthcare
2021 · External reference
Fairness and abstraction in sociotechnical systems
2019 · External reference
“The human body is a black box”: supporting clinical decision-making with deep learning
2019 · External reference
Real-world integration of a sepsis prediction algorithm
2020 · External reference
Artificial intelligence versus clinicians in disease diagnosis: systematic review
2020 · External reference
Unresolved reference
2021 · External reference
A framework for understanding unintended consequences of machine learning
2019 · External reference
Artificial intelligence in medicine
1985 · External reference
High-performance medicine: the convergence of human and artificial intelligence
10.1038/s41591-018-0300-7 · 2019 · External reference
Unresolved reference
2019 · External reference
Unresolved reference
2019 · External reference
Unresolved reference
2021 · External reference
Machine learning in health care: datafication and trust
2022 · External reference
Machine learning in medicine: addressing ethical challenges
10.1371/journal.pmed.1002689 · 2018 · External reference
What this computer needs is a physician
10.1001/jama.2017.19198 · 2018 · External reference
Counterfactual explanations without opening the black box
2018 · External reference
WHO and ITU guidelines on AI for health
2021 · External reference
Do no harm: a roadmap for responsible machine learning for health care
10.1038/s41591-019-0548-6 · 2019 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
2019 · External reference
Artificial intelligence in healthcare
10.1038/s41551-018-0305-z · 2018 · External reference
The false hope of current approaches to explainable AI in health care
10.1016/s2589-7500(21)00208-9 · 2021 · External reference
Big data analytics and AI in healthcare: ethical issues
2021 · External reference
Big data and machine learning in health care
10.1001/jama.2017.18391 · ExternalCitation · doi-reference
What this computer needs is a physician
10.1001/jama.2017.19198 · ExternalCitation · doi-reference
Artificial intelligence in health care: will the value match the hype?
10.1001/jama.2019.4914 · ExternalCitation · doi-reference
Artificial intelligence and black-box medical decisions: accuracy versus explainability
10.1002/hast.973 · ExternalCitation · doi-reference
From what to how: an initial review of AI ethics tools and methods
10.1007/s11948-019-00165-5 · ExternalCitation · doi-reference
Explanation in artificial intelligence: insights from the social sciences
10.1016/j.artint.2018.07.007 · ExternalCitation · doi-reference
The fate of medicine in the time of AI
10.1016/s0140-6736(18)31925-1 · ExternalCitation · doi-reference
The false hope of current approaches to explainable AI in health care
10.1016/s2589-7500(21)00208-9 · ExternalCitation · doi-reference
Dermatologist-level classification of skin cancer with deep neural networks
10.1038/nature21056 · ExternalCitation · doi-reference
Artificial intelligence in healthcare
10.1038/s41551-018-0305-z · ExternalCitation · doi-reference
international evaluation of an AI system for breast cancer screening
10.1038/s41586-019-1799-6 · ExternalCitation · doi-reference
High-performance medicine: the convergence of human and artificial intelligence
10.1038/s41591-018-0300-7 · ExternalCitation · doi-reference
Practical implementation of AI technologies in medicine
10.1038/s41591-018-0307-0 · ExternalCitation · doi-reference
A guide to deep learning in healthcare
10.1038/s41591-018-0316-z · ExternalCitation · doi-reference
Do no harm: a roadmap for responsible machine learning for health care
10.1038/s41591-019-0548-6 · ExternalCitation · doi-reference
Clinical research and ethical integration of healthcare AI
10.1038/s41591-020-1035-9 · ExternalCitation · doi-reference
AI in medicine must be explainable
10.1038/s41591-021-01461-z · ExternalCitation · doi-reference
The “inconvenient truth” about AI in healthcare
10.1038/s41746-019-0155-4 · ExternalCitation · doi-reference
Stop explaining black box machine learning models for high-stakes decisions and use interpretable models instead
10.1038/s42256-019-0048-x · ExternalCitation · doi-reference
The global landscape of AI ethics guidelines
10.1038/s42256-019-0088-2 · ExternalCitation · doi-reference
Machine learning and prediction in medicine
10.1056/nejmp1702071 · ExternalCitation · doi-reference
Implementing machine learning in health care—Addressing ethical challenges
10.1056/nejmp1714229 · ExternalCitation · doi-reference
Regulation of predictive analytics in medicine
10.1126/science.aaw0029 · ExternalCitation · doi-reference
Dissecting racial bias in an algorithm used to manage the health of populations
10.1126/science.aax2342 · ExternalCitation · doi-reference
Against pandemic research exceptionalism
10.1126/science.abc1731 · ExternalCitation · doi-reference
Reproducibility in machine learning for health research
10.1126/scitranslmed.abb1655 · ExternalCitation · doi-reference
On the ethics of algorithmic decision-making in healthcare
10.1136/medethics-2019-105586 · ExternalCitation · doi-reference
Bias in computer systems
10.1145/230538.230561 · ExternalCitation · doi-reference
The mythos of model interpretability
10.1145/3233231 · ExternalCitation · doi-reference
Datasheets for datasets
10.1145/3458723 · ExternalCitation · doi-reference
Mammographic breast density assessment using AI
10.1148/radiol.2018180694 · ExternalCitation · doi-reference
Artificial intelligence-enabled healthcare delivery
10.1177/0141076818815510 · ExternalCitation · doi-reference
Explainability for artificial intelligence in healthcare
10.1186/s12911-020-01332-6 · ExternalCitation · doi-reference
Key challenges for delivering clinical impact with AI
10.1186/s12916-019-1426-2 · ExternalCitation · doi-reference
Machine learning in medicine: addressing ethical challenges
10.1371/journal.pmed.1002689 · ExternalCitation · doi-reference
Beyond adoption: a new framework for theorizing and evaluating nonadoption of health technologies
10.2196/jmir.8775 · ExternalCitation · doi-reference
Delivering CBT using conversational agents
10.2196/mental.7785 · ExternalCitation · doi-reference
Ensuring fairness in machine learning to advance health equity
10.7326/m18-1990 · ExternalCitation · doi-reference
The potential for artificial intelligence in healthcare
10.7861/futurehosp.6-2-94 · ExternalCitation · doi-reference
