Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire

Ongena, Yfke; Haan, Marieke; Yakar, Derya; Kwee, Thomas C.

doi:10.1007/s00330-019-06486-0

Cited by 114 publications

(145 citation statements)

References 20 publications

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“…53 Issues raised by patients include distrust, lack of knowledge, a lack of personal interaction, questions about the efficacy of the AI algorithm, and the importance of being properly informed of its uses. 54 Similar relevant issues were identified by a computer science literature review 55 on human-AI interaction, such as task allocation, lack of knowledge and/or trust, incorrect use due to confusion, and integration issues due to a potentially radically different work practice.…”

Section: Discussionmentioning

confidence: 83%

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Bhandari¹,

Liong

Koppen³

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND: Determination of isocitrate dehydrogenase (IDH) status and, if IDH-mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating imaging features ("radiomics") with genetic status. PURPOSE: Our aim was to perform a diagnostic test accuracy systematic review for classifying IDH and 1p19q status using MR imaging radiomics, to provide future directions for integration into clinical radiology. DATA SOURCES: Ovid (MEDLINE), Scopus, and the Web of Science were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy guidelines. STUDY SELECTION: Fourteen journal articles were selected that included 1655 lower-grade gliomas classified by their IDH and/or 1p19q status from MR imaging radiomic features. DATA ANALYSIS: For each article, the classification of IDH and/or 1p19q status using MR imaging radiomics was evaluated using the area under curve or descriptive statistics. Quality assessment was performed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the radiomics quality score. DATA SYNTHESIS: The best classifier of IDH status was with conventional radiomics in combination with convolutional neural network-derived features (area under the curve ¼ 0.95, 94.4% sensitivity, 86.7% specificity). Optimal classification of 1p19q status occurred with texture-based radiomics (area under the curve ¼ 0.96, 90% sensitivity, 89% specificity). LIMITATIONS: A meta-analysis showed high heterogeneity due to the uniqueness of radiomic pipelines. CONCLUSIONS: Radiogenomics is a potential alternative to standard invasive biopsy techniques for determination of IDH and 1p19q status in lower-grade gliomas but requires translational research for clinical uptake. ABBREVIATIONS: AI ¼ artificial intelligence; AUC ¼ area under the curve; CNN ¼ convolutional neural network; IDH ¼ isocitrate dehydrogenase; IDH-mut ¼ IDH-mutant; LGG ¼ lower-grade gliomas; ML ¼ machine learning; PRISMA-DTA ¼ Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy; QUADAS-2 ¼ Quality Assessment of Diagnostic Accuracy Studies 2; RQS ¼ radiomics quality score; SVM ¼ support vector machine; VASARI ¼ Visually Accessible Rembrandt Images; WHO ¼ World Health Organization

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Section: Discussionmentioning

confidence: 83%

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Bhandari¹,

Liong

Koppen³

et al. 2020

AJNR Am J Neuroradiol

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“…16 Further, most published studies have focused on computer vision health AI applications in radiology and dermatology, which represent only a small fraction of the potential applications of AI in health. [23][24][25] Additionally, there is a need to understand public perspectives versus patient perspectives, because health AI research may rely on large datasets that include information about people who do not have health conditions and/or do not stand to benefit directly from the research. Accordingly, the objective of this study was to learn more about how members of the general public perceive health data being used for AI research.…”

Section: Open Accessmentioning

confidence: 99%

Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research

2020

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ObjectivesGiven widespread interest in applying artificial intelligence (AI) to health data to improve patient care and health system efficiency, there is a need to understand the perspectives of the general public regarding the use of health data in AI research.DesignA qualitative study involving six focus groups with members of the public. Participants discussed their views about AI in general, then were asked to share their thoughts about three realistic health AI research scenarios. Data were analysed using qualitative description thematic analysis.SettingsTwo cities in Ontario, Canada: Sudbury (400 km north of Toronto) and Mississauga (part of the Greater Toronto Area).ParticipantsForty-one purposively sampled members of the public (21M:20F, 25–65 years, median age 40).ResultsParticipants had low levels of prior knowledge of AI and mixed, mostly negative, perceptions of AI in general. Most endorsed using data for health AI research when there is strong potential for public benefit, providing that concerns about privacy, commercial motives and other risks were addressed. Inductive thematic analysis identified AI-specific hopes (eg, potential for faster and more accurate analyses, ability to use more data), fears (eg, loss of human touch, skill depreciation from over-reliance on machines) and conditions (eg, human verification of computer-aided decisions, transparency). There were mixed views about whether data subject consent is required for health AI research, with most participants wanting to know if, how and by whom their data were used. Though it was not an objective of the study, realistic health AI scenarios were found to have an educational effect.ConclusionsNotwithstanding concerns and limited knowledge about AI in general, most members of the general public in six focus groups in Ontario, Canada perceived benefits from health AI and conditionally supported the use of health data for AI research.

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“…Tran, et al found that after direct experience with medical AI patients saw AI as a great opportunity for improvement in medicine; it would improve follow-up care, reduce burdens of treatments and help facilitate physicians' work [4]. Patients are reluctant to rely on AI-supported medical care even when this kind of technology is proven to outperform human doctors [4][5][6][7]. Ongena, et al recommended future studies to help doctors and healthcare systems to identify ways to help patients overcome the distrust of AI technology [6].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Patients are reluctant to rely on AI-supported medical care even when this kind of technology is proven to outperform human doctors [4][5][6][7]. Ongena, et al recommended future studies to help doctors and healthcare systems to identify ways to help patients overcome the distrust of AI technology [6]. Recognizing factors that contribute to patients' distrust will benefit patients and facilitate implementation of medical AI patient protocols for doctors, healthcare facilities and AI researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding Patient Perception of Medical Artificial Intelligence: A Proposed Qualitative Study

Escobar

2020

Proceedings of the 38th ACM International Conference on Design of Communication

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Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire

Cited by 114 publications

References 20 publications

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research

Understanding Patient Perception of Medical Artificial Intelligence: A Proposed Qualitative Study

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