Ethics Guidelines for Trustworthy AI

Cannarsa, Michel

doi:10.1017/9781108936040.022

Cited by 56 publications

(34 citation statements)

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“…The European Commission's Joint Research Centre, for instance, calls for an ethics and trust framework that will enable the safe use of AI and ML, while the US Department of Health and Human Services vows to support AI systems that inspire trust and respect privacy and security [107,108]. The High-level Expert Group on Artificial Intelligence established by the European Commission has issued Ethical Guidelines for Trustworthy AI [109]. While not specific to health-related uses of AI, such guidelines emphasize the centrality of trustworthiness.…”

Section: Plos Digital Healthmentioning

confidence: 99%

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

et al. 2023

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Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field’s potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.

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Section: Plos Digital Healthmentioning

confidence: 99%

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

et al. 2023

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Section: Challenges and Perspectivesmentioning

confidence: 99%

“…On 8 April 2019, the High-Level Expert Group on AI set up by the European Commission presented a programmatic document which should be considered as a reference guideline to handle AI integration in various fields, including healthcare, aiming to increase individual and collective human well-being [ 119 ]. This document was followed by the White Paper on Artificial Intelligence in 2020 [ 119 ]. According to these documents, reliable AI should be: (1) lawful—respecting all applicable laws and regulations, including privacy requirements; (2) ethical—respecting ethical principles and values; and (3) robust—both from a technical point of view, and considering its social environment and explainability [ 119 ].…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

“…This document was followed by the White Paper on Artificial Intelligence in 2020 [ 119 ]. According to these documents, reliable AI should be: (1) lawful—respecting all applicable laws and regulations, including privacy requirements; (2) ethical—respecting ethical principles and values; and (3) robust—both from a technical point of view, and considering its social environment and explainability [ 119 ].…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

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Artificial Intelligence in Emergency Radiology: Where Are We Going?

et al. 2022

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Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients’ lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS–PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients’ clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease’s severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.

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“…6 It goes on to specify that humans should be able to interpret, accept, disregard, override or reverse DSS outputs. Similarly, the EU High-Level Expert Group 7 has emphasized the importance of human oversight for trustworthy AI, for instance by having humans "in-the-loop" (i.e., humans monitoring the system's operation and intervening during the decision cycle).…”

Section: Introductionmentioning

confidence: 99%

Reflection Machines: Supporting Effective Human Oversight Over Medical Decision Support Systems

Haselager

Schraffenberger

Thill

et al. 2023

Camb Q Healthc Ethics

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Human decisions are increasingly supported by decision support systems (DSS). Humans are required to remain “on the loop,” by monitoring and approving/rejecting machine recommendations. However, use of DSS can lead to overreliance on machines, reducing human oversight. This paper proposes “reflection machines” (RM) to increase meaningful human control. An RM provides a medical expert not with suggestions for a decision, but with questions that stimulate reflection about decisions. It can refer to data points or suggest counterarguments that are less compatible with the planned decision. RMs think against the proposed decision in order to increase human resistance against automation complacency. Building on preliminary research, this paper will (1) make a case for deriving a set of design requirements for RMs from EU regulations, (2) suggest a way how RMs could support decision-making, (3) describe the possibility of how a prototype of an RM could apply to the medical domain of chronic low back pain, and (4) highlight the importance of exploring an RM’s functionality and the experiences of users working with it.

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Ethics Guidelines for Trustworthy AI

Cited by 56 publications

References 0 publications

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

Artificial Intelligence in Emergency Radiology: Where Are We Going?

Reflection Machines: Supporting Effective Human Oversight Over Medical Decision Support Systems

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