[HTML][HTML] Ethics of AI in radiology: a review of ethical and societal implications
M Goisauf, M Cano Abadía - Frontiers in Big Data, 2022 - frontiersin.org
Artificial intelligence (AI) is being applied in medicine to improve healthcare and advance
health equity. The application of AI-based technologies in radiology is expected to improve …
health equity. The application of AI-based technologies in radiology is expected to improve …
[HTML][HTML] Significance of machine learning in healthcare: Features, pillars and applications
Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
Current advancement in diagnosing atrial fibrillation by utilizing wearable devices and artificial intelligence: A review study
Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than
80 years old. The importance of early diagnosis of atrial fibrillation has been broadly …
80 years old. The importance of early diagnosis of atrial fibrillation has been broadly …
Interpretable machine learning for early prediction of prognosis in sepsis: a discovery and validation study
Introduction This study aimed to develop and validate an interpretable machine-learning
model based on clinical features for early predicting in-hospital mortality in critically ill …
model based on clinical features for early predicting in-hospital mortality in critically ill …
Bridging the chasm between AI and clinical implementation
Many advances in artificial intelligence (AI) for health care using deep neural networks have
been commercialised. But few AI tools have been implemented in health systems. Why has …
been commercialised. But few AI tools have been implemented in health systems. Why has …
Defining the undefinable: the black box problem in healthcare artificial intelligence
JJ Wadden - Journal of Medical Ethics, 2022 - jme.bmj.com
The 'black box problem'is a long-standing talking point in debates about artificial intelligence
(AI). This is a significant point of tension between ethicists, programmers, clinicians and …
(AI). This is a significant point of tension between ethicists, programmers, clinicians and …
The role of explainability in assuring safety of machine learning in healthcare
Established approaches to assuring safety-critical systems and software are difficult to apply
to systems employing ML where there is no clear, pre-defined specification against which to …
to systems employing ML where there is no clear, pre-defined specification against which to …
“Just” accuracy? Procedural fairness demands explainability in AI-based medical resource allocations
The increasing application of artificial intelligence (AI) to healthcare raises both hope and
ethical concerns. Some advanced machine learning methods provide accurate clinical …
ethical concerns. Some advanced machine learning methods provide accurate clinical …
Algorithms for ethical decision-making in the clinic: A proof of concept
Abstract Machine intelligence already helps medical staff with a number of tasks. Ethical
decision-making, however, has not been handed over to computers. In this proof-of-concept …
decision-making, however, has not been handed over to computers. In this proof-of-concept …
Black box prediction methods in sports medicine deserve a red card for reckless practice: a change of tactics is needed to advance athlete care
GS Bullock, T Hughes, AH Arundale, P Ward… - Sports Medicine, 2022 - Springer
There is growing interest in the role of predictive analytics in sport, where such extensive
data collection provides an exciting opportunity for the development and utilisation of …
data collection provides an exciting opportunity for the development and utilisation of …