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- Olczak, Jakub, et al.
(författare)
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Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders : an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal
- 2021
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Ingår i: Acta Orthopaedica. - : Taylor & Francis. - 1745-3674 .- 1745-3682. ; 92:5, s. 513-525
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Tidskriftsartikel (refereegranskat)abstract
- Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards healthcare professionals, the difference in competencies and goals creates challenges for collaboration and knowledge exchange. We aim to provide clinicians with a context and understanding of AI research by facilitating communication between creators, researchers, clinicians, and readers of medical AI and ML research. Methods and results - We present the common tasks, considerations, and pitfalls (both methodological and ethical) that clinicians will encounter in AI research. We discuss the following topics: labeling, missing data, training, testing, and overfitting. Common performance and outcome measures for various AI and ML tasks are presented, including accuracy, precision, recall, F1 score, Dice score, the area under the curve, and ROC curves. We also discuss ethical considerations in terms of privacy, fairness, autonomy, safety, responsibility, and liability regarding data collecting or sharing. Interpretation - We have developed guidelines for reporting medical AI research to clinicians in the run-up to a broader consensus process. The proposed guidelines consist of a Clinical Artificial Intelligence Research (CAIR) checklist and specific performance metrics guidelines to present and evaluate research using AI components. Researchers, engineers, clinicians, and other stakeholders can use these proposal guidelines and the CAIR checklist to read, present, and evaluate AI research geared towards a healthcare setting.
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| 2. |
- Bosson, J. K., et al.
(författare)
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Psychometric Properties and Correlates of Precarious Manhood Beliefs in 62 Nations
- 2021
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Ingår i: Journal of Cross-Cultural Psychology. - : SAGE Publications. - 0022-0221 .- 1552-5422. ; 52:3
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Tidskriftsartikel (refereegranskat)abstract
- Precarious manhood beliefs portray manhood, relative to womanhood, as a social status that is hard to earn, easy to lose, and proven via public action. Here, we present cross-cultural data on a brief measure of precarious manhood beliefs (the Precarious Manhood Beliefs scale [PMB]) that covaries meaningfully with other cross-culturally validated gender ideologies and with country-level indices of gender equality and human development. Using data from university samples in 62 countries across 13 world regions (N = 33,417), we demonstrate: (1) the psychometric isomorphism of the PMB (i.e., its comparability in meaning and statistical properties across the individual and country levels); (2) the PMB's distinctness from, and associations with, ambivalent sexism and ambivalence toward men; and (3) associations of the PMB with nation-level gender equality and human development. Findings are discussed in terms of their statistical and theoretical implications for understanding widely-held beliefs about the precariousness of the male gender role.
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| 3. |
- Chatzipanagiotou, Marita, et al.
(författare)
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Automated recognition of geographical named entities in titles of Ukiyo-e prints
- 2021
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Ingår i: Journal of the ACM / Association for Computing Machinery. - New York, NY, USA : ACM. - 0004-5411. ; , s. 70-77
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Tidskriftsartikel (refereegranskat)abstract
- This paper investigates the application of Natural Language Processing as a means to study the relationship between topography and its visual renderings in early modern Japanese ukiyo-e landscape prints. We introduce a new dataset with titles of landscape prints that have been annotated by an art historian for any included place-names. The prints are hosted by the digital database of the Art Research Center at the Ritsumeikan University, Kyoto, one of the hubs of Digital Humanities in Japan. By applying, calibrating and assessing a Named Entity Recognition (NER) tool, we argue that ‘distant viewing’ or macroanalysis of visual datasets can be facilitated, which is needed to assist art historical studies of this rich, complex and diverse research material. Experimental results indicated that the performance of NER can be improved by 30% and reach 50% precision, by using part of the introduced dataset.
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