@article{hatherley_limits_2020,

title = {Limits of trust in medical AI},

author = {Joshua James Hatherley},

url = {https://jme.bmj.com/lookup/doi/10.1136/medethics-2019-105935},

doi = {10.1136/medethics-2019-105935},

issn = {0306-6800, 1473-4257},

year  = {2020},

date = {2020-07-01},

urldate = {2025-03-26},

journal = {Journal of Medical Ethics},

volume = {46},

number = {7},

pages = {478\textendash481},

abstract = {Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hatherley_limits_2020-1,

title = {Limits of trust in medical AI},

author = {Joshua James Hatherley},

url = {https://jme.bmj.com/lookup/doi/10.1136/medethics-2019-105935},

doi = {10.1136/medethics-2019-105935},

issn = {0306-6800, 1473-4257},

year  = {2020},

date = {2020-07-01},

urldate = {2025-03-26},

journal = {Journal of Medical Ethics},

volume = {46},

number = {7},

pages = {478\textendash481},

abstract = {Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{roberts_knowledge_2020,

title = {Knowledge, reliability, and the admissibility of forensic science evidence},

author = {Andrew Roberts},

url = {https://www.tandfonline.com/doi/full/10.1080/00450618.2020.1729238},

doi = {10.1080/00450618.2020.1729238},

issn = {0045-0618, 1834-562X},

year  = {2020},

date = {2020-05-01},

urldate = {2021-11-10},

journal = {Australian Journal of Forensic Sciences},

volume = {52},

number = {3},

pages = {269\textendash274},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pink_making_2020,

title = {Making Spaces for Staff Breaks: A Design Anthropology Approach},

author = {Sarah Pink and Melisa Duque and Shanti Sumartojo and Laurene Vaughan},

url = {http://journals.sagepub.com/doi/10.1177/1937586719900954},

doi = {10.1177/1937586719900954},

issn = {1937-5867, 2167-5112},

year  = {2020},

date = {2020-04-01},

urldate = {2021-11-10},

journal = {HERD: Health Environments Research \& Design Journal},

volume = {13},

number = {2},

pages = {243\textendash255},

abstract = {Purpose: 

 This article proposes and demonstrates a design anthropological approach to hospital design and architecture and engages this approach to advance recent discussions of the question of designing for staff breaks. 

 Background: 

 We respond to calls for attention to sensory and experiential dimensions of hospital architecture and design through social science approaches and to research into the sensory environments for staff breaks. 

 Method: 

 Design anthropology enables us to surface the experiential and unspoken knowledge and practice of hospital staff, which is inaccessible through conventional consultations, quantitative post-occupancy evaluation surveys, or traditional interviews. We draw on ethnographic research into the everyday experience, improvisatory activity, and imagined futures of staff working in the psychiatric department of a large new architecturally designed hospital in Australia. 

 Results: 

 We argue that while the sensory aspects of hospital design conventionally cited\textemdashsuch as light and green areas\textemdashare relevant, attention to staff priorities that emerge in practice reveals that well-being is contingent on other qualities and resources. 

 Conclusions: 

 This suggests a refocus, away from the idea that environments impact on staff to create well-being, to understanding how staff improvise to create environments of well-being. We outline the implications of this research for an agenda for design for well-being in which architects and designers are often constrained by generic design briefs to argue for a shift in policy that attends more deeply to staff as future users of hospital designs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{yunzhe_exploiting_2020,

title = {Exploiting patterns to explain individual predictions},

author = {Jia Yunzhe and Cameron Bailey and Kotagiri Romamohanarao and Christopher Leckie},

url = {https://www.researchgate.net/publication/333669073_Exploiting_patterns_to_explain_individual_predictions},

doi = {10.1007/s10115-019-01368-9},

year  = {2020},

date = {2020-03-01},

journal = {Knowledge and Information System},

volume = {62},

number = {1},

pages = {1\textendash24},

abstract = {Users need to understand the predictions of a classifier, especially when decisions based on the predictions can have severe consequences. The explanation of a prediction reveals the reason why a classifier makes a certain prediction, and it helps users to accept or reject the prediction with greater confidence. This paper proposes an explanation method called Pattern Aided Local Explanation (PALEX) to provide instance-level explanations for any classifier. PALEX takes a classifier, a test instance and a frequent pattern set summarizing the training data of the classifier as inputs, and then outputs the supporting evidence that the classifier considers important for the prediction of the instance. To study the local behavior of a classifier in the vicinity of the test instance, PALEX uses the frequent pattern set from the training data as an extra input to guide generation of new synthetic samples in the vicinity of the test instance. Contrast patterns are also used in PALEX to identify locally discriminative features in the vicinity of a test instance. PALEX is particularly effective for scenarios where there exist multiple explanations. In our experiments, we compare PALEX to several state-of-the-art explanation methods over a range of benchmark datasets and find that it can identify explanations with both high precision and high recall.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zameni_unsupervised_2020,

title = {Unsupervised online change point detection in high-dimensional time series},

author = {Masoomeh Zameni and Amin Sadri and Zahra Ghafoori and Masud Moshtaghi and Flora D. Salim and Christopher Leckie and Kotagiri Ramamohanarao},

url = {http://link.springer.com/10.1007/s10115-019-01366-x},

doi = {10.1007/s10115-019-01366-x},

issn = {0219-1377, 0219-3116},

year  = {2020},

date = {2020-02-01},

urldate = {2023-11-30},

journal = {Knowledge and Information Systems},

volume = {62},

number = {2},

pages = {719\textendash750},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{richardson_privacy_2020,

title = {Privacy and the Law \textendash Introduction},

author = {Megan Richardson},

url = {http://sites.thomsonreuters.com.au/journals/tag/privacy-and-the-law-introduction-by-guest-editor-of-this-issue-professor-megan-richardson/},

year  = {2020},

date = {2020-01-01},

urldate = {2021-11-11},

journal = {AUSTRALIAN LAW JOURNAL},

volume = {94},

number = {10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kang_understanding_2020,

title = {Understanding and improving ontology reasoning efficiency through learning and ranking},

author = {Yong-Bin Kang and Shonali Krishnaswamy and Wudhichart Sawangphol and Lianli Gao and Yuan-Fang Li},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0306437917306476},

doi = {10.1016/j.is.2019.07.002},

issn = {03064379},

year  = {2020},

date = {2020-01-01},

urldate = {2021-11-10},

journal = {Information Systems},

volume = {87},

pages = {101412},

abstract = {Ontologies are the fundamental building blocks of the Semantic Web and Linked Data. Reasoning is critical to ensure the logical consistency of ontologies, and to compute inferred knowledge from an ontology. It has been shown both theoretically and empirically that, despite decades of intensive work on optimising ontology reasoning algorithms, performing core reasoning tasks on large and expressive ontologies is time-consuming and resource-intensive. In this paper, we present the meta-reasoning framework RO* to tackle the important problems of understanding the source of TBox reasoning hardness and predicting and optimising TBox reasoning efficiency by exploiting machine learning techniques. RO* combines state-of-the-art OWL 2 DL reasoners as well as an efficient OWL 2 EL reasoner as components, and predicts the most efficient one by using an ensemble of robust learning algorithms including XGBoost and Random Forests. A comprehensive evaluation on a large and carefully curated ontology corpus shows that RO 

* outperforms all six component reasoners as well as AutoFolio, a robust and strong algorithm selection system. 

 Previous article in issue},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jayaraman_healthcare_2020,

title = {Healthcare 4.0: A review of frontiers in digital health},

author = {Prem Prakash Jayaraman and Abdur Rahim Mohammad Forkan and Ahsan Morshed and Pari Delir Haghighi and Yong-Bin Kang},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1350},

doi = {10.1002/widm.1350},

issn = {1942-4795},

year  = {2020},

date = {2020-01-01},

urldate = {2021-11-10},

journal = {WIREs Data Mining and Knowledge Discovery},

volume = {10},

number = {2},

pages = {e1350},

abstract = {Healthcare 4.0 is a term that has emerged recently and derived from Industry 4.0. Today, the health care sector is more digital than in past decades; for example, spreading from x-rays and magnetic resonance imaging to computed tomography and ultrasound scans to electric medical records. With the wide spectrum of digital technologies underpinning Healthcare 4.0 to deliver more effective and efficient health care services, in this article, we use the wisdom pyramid methodology to conduct a systematic review of current digital frontiers in Healthcare 4.0. This article is categorized under: Technologies \> Computer Architectures for Data Mining Application Areas \> Health Care Application Areas \> Data Mining Software Tools Fundamental Concepts of Data and Knowledge \> Knowledge Representation},

note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1350},

keywords = {},

pubstate = {published},

tppubtype = {article}

}