Artificial intelligence in neuroradiology: a scoping review of some ethical challenges

Khosravi, Pegah; Schweitzer, Mark E.

doi:10.3389/fradi.2023.1149461

Cited by 6 publications

(2 citation statements)

References 76 publications

(94 reference statements)

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“…Data availability is another key component of the reproducibility of AI research studies because AI models may have variable performance based on the datasets used. However, less than one-third of the articles share code (code sharing) and adequately document methods [ 13 ]. With this knowledge, it is difficult to establish that AI findings are always generalizable across different populations.…”

Section: Introductionmentioning

confidence: 99%

Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare

Geny,

Andres,

Talha

et al. 2024

Sensors

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In the last few decades, there has been an ongoing transformation of our healthcare system with larger use of sensors for remote care and artificial intelligence (AI) tools. In particular, sensors improved by new algorithms with learning capabilities have proven their value for better patient care. Sensors and AI systems are no longer only non-autonomous devices such as the ones used in radiology or surgical robots; there are novel tools with a certain degree of autonomy aiming to largely modulate the medical decision. Thus, there will be situations in which the doctor is the one making the decision and has the final say and other cases in which the doctor might only apply the decision presented by the autonomous device. As those are two hugely different situations, they should not be treated the same way, and different liability rules should apply. Despite a real interest in the promise of sensors and AI in medicine, doctors and patients are reluctant to use it. One important reason is a lack clear definition of liability. Nobody wants to be at fault, or even prosecuted, because they followed the advice from an AI system, notably when it has not been perfectly adapted to a specific patient. Fears are present even with simple sensors and AI use, such as during telemedicine visits based on very useful, clinically pertinent sensors; with the risk of missing an important parameter; and, of course, when AI appears “intelligent”, potentially replacing the doctors’ judgment. This paper aims to provide an overview of the liability of the health professional in the context of the use of sensors and AI tools in remote healthcare, analyzing four regimes: the contract-based approach, the approach based on breach of duty to inform, the fault-based approach, and the approach related to the good itself. We will also discuss future challenges and opportunities in the promising domain of sensors and AI use in medicine.

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Section: Introductionmentioning

confidence: 99%

Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare

Geny,

Andres,

Talha

et al. 2024

Sensors

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“…To achieve this, collaborative efforts involving researchers, radiologists, and policymakers are crucial to ensure the effective integration of AI algorithms into routine clinical practice. 10 Robust validation studies encompassing diverse patient populations and different imaging systems are vital to establish the generalizability and reliability of AI models in real-world scenarios.…”

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confidence: 99%

Editorial for “Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter‐Observer Variability”

Khosravi

2023

Magnetic Resonance Imaging

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AI‐Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches

Khosravi,

Mohammadi,

Zahiri

et al. 2024

Magnetic Resonance Imaging

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Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far‐reaching implications across various medical fields. This review meticulously examines the integration of artificial intelligence (AI) in anomaly detection for MR images, spotlighting its transformative impact on medical diagnostics. We delve into the forefront of AI applications in MRI, exploring advanced machine learning (ML) and deep learning (DL) methodologies that are pivotal in enhancing the precision of diagnostic processes. The review provides a detailed analysis of preprocessing, feature extraction, classification, and segmentation techniques, alongside a comprehensive evaluation of commonly used metrics. Further, this paper explores the latest developments in ensemble methods and explainable AI, offering insights into future directions and potential breakthroughs. This review synthesizes current insights, offering a valuable guide for researchers, clinicians, and medical imaging experts. It highlights AI's crucial role in improving the precision and speed of detecting key structural and functional irregularities in MRI. Our exploration of innovative techniques and trends furthers MRI technology development, aiming to refine diagnostics, tailor treatments, and elevate patient care outcomes.Level of Evidence5Technical EfficacyStage 1.

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Artificial intelligence in neuroradiology: a scoping review of some ethical challenges

Cited by 6 publications

References 76 publications

Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare

Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare

Editorial for “Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter‐Observer Variability”

AI‐Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches

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