scholarly journals Breast screening and artificial intelligence: an independent evaluation of two different software carried out at Valenciennes hospital (erratum)

2020 ◽  
Author(s):  
Adrien Le Vourch ◽  
Poncelet Edouard ◽  
Nicolas Laurent
Keyword(s):  
Artificial Intelligence ◽  
Breast Screening ◽  
Independent Evaluation

scholarly journals Human factors challenges for the safe use of artificial intelligence in patient care

2019 ◽  
Vol 26 (1) ◽  
pp. e100081 ◽  
Author(s):  
Mark Sujan ◽  
Dominic Furniss ◽  
Kath Grundy ◽  
Howard Grundy ◽  
David Nelson ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Patient Care ◽  
Situation Awareness ◽  
Human Performance ◽  
Independent Evaluation ◽  
The Impact ◽  
Human Factors Research

The use of artificial intelligence (AI) in patient care can offer significant benefits. However, there is a lack of independent evaluation considering AI in use. The paper argues that consideration should be given to how AI will be incorporated into clinical processes and services. Human factors challenges that are likely to arise at this level include cognitive aspects (automation bias and human performance), handover and communication between clinicians and AI systems, situation awareness and the impact on the interaction with patients. Human factors research should accompany the development of AI from the outset.

scholarly journals Artificial intelligence and breast screening: French Radiology Community position paper

2019 ◽  
Vol 100 (10) ◽  
pp. 553-566 ◽  
Author(s):  
I. Thomassin-Naggara ◽  
C. Balleyguier ◽  
L. Ceugnart ◽  
P. Heid ◽  
G. Lenczner ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Breast Screening ◽  
Position Paper

scholarly journals Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations

2019 ◽  
Vol 9 (2) ◽  
pp. 110 ◽  
Author(s):  
Meng-Leong HOW ◽  
Wei Loong David HUNG
Keyword(s):  
Artificial Intelligence ◽  
Adaptive Learning ◽  
Large Scale ◽  
Small Sample Size ◽  
Small Sample ◽  
Learning System ◽  
Learning Needs ◽  
Educational Stakeholders ◽  
Independent Evaluation ◽  
Computer Scientists

Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved for large-scale deployment. Beyond simply believing in the information provided by the AI-ALS supplier, there arises a need for educational stakeholders to independently understand the motif of the pedagogical characteristics that underlie the AI-ALS. Laudable efforts were made by researchers to engender frameworks for the evaluation of AI-ALS. Nevertheless, those highly technical techniques often require advanced mathematical knowledge or computer programming skills. There remains a dearth in the extant literature for a more intuitive way for educational stakeholders—rather than computer scientists—to carry out the independent evaluation of an AI-ALS to understand how it could provide opportunities to educe the problem-solving abilities of the students so that they can successfully learn the subject matter. This paper proffers an approach for educational stakeholders to employ Bayesian networks to simulate predictive hypothetical scenarios with controllable parameters to better inform them about the suitability of the AI-ALS for the students.

scholarly journals Screening participants’ attitudes to the introduction of artificial intelligence in breast screening

2021 ◽  
pp. 096914132110014
Author(s):  
Clarisse F de Vries ◽  
Brian E Morrissey ◽  
Donna Duggan ◽  
Roger T Staff ◽  
Gerald Lip
Keyword(s):  
Artificial Intelligence ◽  
Breast Screening
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