Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://pubmed.ncbi.nlm.nih.gov/34648520/ below:

A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts

Affiliations

• ¹ Mary Ann & J. Milburn Smith Child Health Outcomes, Research and Evaluation Center, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, United States of America.
• ² Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America.
• ³ Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
• ⁴ Medical Imaging Research Center, MIRC, KU Leuven, Leuven, Belgium.
• ⁵ Department of Human Genetics, KU Leuven, Leuven, Belgium.
• ⁶ Sage Bionetworks, Seattle, Washington, United States of America.
• ⁷ School for the Future of Innovation in Society, Arizona State University, Washington, District of Columbia, United States of America.
• ⁸ Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, North Carolina, United States of America.
• ⁹ Levin College of Law, University of Florida, Gainesville, Florida, United States of America.
• ¹⁰ Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America.
• ¹¹ Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
• ¹² School of Engineering Design, Technology, and Professional Programs, Pennsylvania State University, University Park, Pennsylvania, United States of America.

• PMID: 34648520
• PMCID: PMC8516205
• DOI: 10.1371/journal.pone.0257923

Item in Clipboard

Sara H Katsanis et al. PLoS One. 2021.

• ¹ Mary Ann & J. Milburn Smith Child Health Outcomes, Research and Evaluation Center, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, United States of America.
• ² Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America.
• ³ Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
• ⁴ Medical Imaging Research Center, MIRC, KU Leuven, Leuven, Belgium.
• ⁵ Department of Human Genetics, KU Leuven, Leuven, Belgium.
• ⁶ Sage Bionetworks, Seattle, Washington, United States of America.
• ⁷ School for the Future of Innovation in Society, Arizona State University, Washington, District of Columbia, United States of America.
• ⁸ Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, North Carolina, United States of America.
• ⁹ Levin College of Law, University of Florida, Gainesville, Florida, United States of America.
• ¹⁰ Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America.
• ¹¹ Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
• ¹² School of Engineering Design, Technology, and Professional Programs, Pennsylvania State University, University Park, Pennsylvania, United States of America.

• PMID: 34648520
• PMCID: PMC8516205
• DOI: 10.1371/journal.pone.0257923

Item in Clipboard

• Correction: A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts.

  PLOS ONE Staff. PLOS ONE Staff. PLoS One. 2021 Dec 16;16(12):e0261738. doi: 10.1371/journal.pone.0261738. eCollection 2021. PLoS One. 2021. PMID: 34914792 Free PMC article.

Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States. We used Qualtrics Panels to collect responses from general public respondents using two complementary and overlapping survey instruments; one focused on six types of biometrics (including facial images and DNA) and their uses in a wide range of societal contexts (including healthcare and research) and the other focused on facial imaging, facial recognition technology, and related data practices in health and research contexts specifically. We collected responses from a diverse group of 4,048 adults in the United States (2,038 and 2,010, from each survey respectively). A majority of respondents (55.5%) indicated they were equally worried about the privacy of medical records, DNA, and facial images collected for precision health research. A vignette was used to gauge willingness to participate in a hypothetical precision health study, with respondents split as willing to (39.6%), unwilling to (30.1%), and unsure about (30.3%) participating. Nearly one-quarter of respondents (24.8%) reported they would prefer to opt out of the DNA component of a study, and 22.0% reported they would prefer to opt out of both the DNA and facial imaging component of the study. Few indicated willingness to pay a fee to opt-out of the collection of their research data. Finally, respondents were offered options for ideal governance design of their data, as "open science"; "gated science"; and "closed science." No option elicited a majority response. Our findings indicate that while a majority of research participants might be comfortable with facial images and facial recognition technologies in healthcare and health-related research, a significant fraction expressed concern for the privacy of their own face-based data, similar to the privacy concerns of DNA data and medical records. A nuanced approach to uses of face-based data in healthcare and health-related research is needed, taking into consideration storage protection plans and the contexts of use.

PubMed Disclaimer

JT discloses a consulting relationship with Genetika, and JKW discloses employment with Geisinger. The authors have declared that no other competing interests exist.

Respondents were asked the following question: “There are many different types of facial imaging, and the ability to identify an individual from these different types varies. In health research, names and other identifying information are removed from facial imaging to preserve the privacy of the individual. However, reidentification is sometimes possible. Imagine that you are participating in health research involving facial images and facial imaging data. Based on what you know, how concerned are you about your privacy if the following are used and shared as part of that health research?” Proportion of respondents indicating they were very concerned (dark orange), somewhat concerned (light orange), not too concerned (light blue), and not at all concerned (dark blue) are displayed along with item nonresponses (gray).

For each scenario, respondents…

For each scenario, respondents reported whether the use case was acceptable (blue) or unacceptable (orange) or reported being unsure (gray) about the (un)acceptability of FRT.

Respondents were presented with…

Respondents were presented with this vignette: “For the next set of questions, imagine that you are asked to participate in a health study that seeks to understand a wide range of human diseases and conditions. The researchers will study your medical records (such test results and information about diseases and conditions); will collect a DNA sample to study your DNA information; and will collect images of your face to study along with any medical images and related information in your medical records. The researchers will not share study resources that could identify someone easily. Each participant will be given a unique study ID number.” Shades of blue denote those definitely (dark) and probably (light) willing to participate. Shades of orange denote those definitely (dark) and probably (light) not willing to participate. Gray shading denotes those not sure about participation.

Blue shading denotes the proportion of respondents who expressed they were very (dark blue) and somewhat (light blue) comfortable with a study’s use of each of the three types of research resources. Orange shading denotes the proportion of respondents who expressed they were very (dark orange) and somewhat (light orange) uncomfortable with a study’s use of each of the three types of research resources.

The darkest shade…

The darkest shade of blue denotes equal concern for the three types of research resources (medical records, DNA, and facial images). The second darkest shade of blue denotes those whose concerns for medical records were stronger (more) than their concerns about either facial images or DNA. The second lightest shade of blue denotes those whose concerns for facial images were stronger (more) than their concerns about either medical records or DNA. The lightest shade of blue denotes those whose concerns for DNA were stronger (more) than their concerns about either medical records or facial images.

The…

The darkest shade of blue denotes willingness to participate fully including with the three types of research information (medical record, DNA, and facial imaging information). The second darkest shade of blue denotes those who would opt out of the facial imaging component. The second lightest shade of blue denotes those who would opt out of the DNA component. The lightest shade of blue denotes those who would opt out of both the DNA and facial imaging component.

The darkest blue shading denotes the…

The darkest blue shading denotes the most open of the three options, that resources should be unrestricted and made available to as many researchers as possible to answer as many research questions as possible even if that increases privacy risks to participants. The moderate blue shading denotes that research resources should be controlled and made available to qualified researchers to answer any research questions reasonably related to human health. The lightest blue shading denotes that research resources should be restricted and made available only to a few researchers and only to answer research questions stated at the beginning of the study to reduce privacy risks to participants.

• Public Attitudes toward Consent and Data Sharing in Biobank Research: A Large Multi-site Experimental Survey in the US.

  Sanderson SC, Brothers KB, Mercaldo ND, Clayton EW, Antommaria AHM, Aufox SA, Brilliant MH, Campos D, Carrell DS, Connolly J, Conway P, Fullerton SM, Garrison NA, Horowitz CR, Jarvik GP, Kaufman D, Kitchner TE, Li R, Ludman EJ, McCarty CA, McCormick JB, McManus VD, Myers MF, Scrol A, Williams JL, Shrubsole MJ, Schildcrout JS, Smith ME, Holm IA. Sanderson SC, et al. Am J Hum Genet. 2017 Mar 2;100(3):414-427. doi: 10.1016/j.ajhg.2017.01.021. Epub 2017 Feb 9. Am J Hum Genet. 2017. PMID: 28190457 Free PMC article. Clinical Trial.

• Evolving public views on the value of one's DNA and expectations for genomic database governance: Results from a national survey.

  Briscoe F, Ajunwa I, Gaddis A, McCormick J. Briscoe F, et al. PLoS One. 2020 Mar 11;15(3):e0229044. doi: 10.1371/journal.pone.0229044. eCollection 2020. PLoS One. 2020. PMID: 32160204 Free PMC article.

• Public responses to the sharing and linkage of health data for research purposes: a systematic review and thematic synthesis of qualitative studies.

  Aitken M, de St Jorre J, Pagliari C, Jepson R, Cunningham-Burley S. Aitken M, et al. BMC Med Ethics. 2016 Nov 10;17(1):73. doi: 10.1186/s12910-016-0153-x. BMC Med Ethics. 2016. PMID: 27832780 Free PMC article. Review.

• Limits of data anonymity: lack of public awareness risks trust in health system activities.

  Gille F, Brall C. Gille F, et al. Life Sci Soc Policy. 2021 Jul 26;17(1):7. doi: 10.1186/s40504-021-00115-9. Life Sci Soc Policy. 2021. PMID: 34304736 Free PMC article. Review.

• Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology.

  Naqvi S, Hoskens H, Wilke F, Weinberg SM, Shaffer JR, Walsh S, Shriver MD, Wysocka J, Claes P. Naqvi S, et al. Annu Rev Genomics Hum Genet. 2022 Aug 31;23:383-412. doi: 10.1146/annurev-genom-120121-102607. Epub 2022 Apr 28. Annu Rev Genomics Hum Genet. 2022. PMID: 35483406 Free PMC article. Review.

• Review on Facial-Recognition-Based Applications in Disease Diagnosis.

  Qiang J, Wu D, Du H, Zhu H, Chen S, Pan H. Qiang J, et al. Bioengineering (Basel). 2022 Jun 23;9(7):273. doi: 10.3390/bioengineering9070273. Bioengineering (Basel). 2022. PMID: 35877324 Free PMC article. Review.

• Correction: A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts.

  PLOS ONE Staff. PLOS ONE Staff. PLoS One. 2021 Dec 16;16(12):e0261738. doi: 10.1371/journal.pone.0261738. eCollection 2021. PLoS One. 2021. PMID: 34914792 Free PMC article.

• The future is yesterday: Use of AI-driven facial recognition to enhance value in the travel and tourism industry.

  Gupta S, Modgil S, Lee CK, Sivarajah U. Gupta S, et al. Inf Syst Front. 2023;25(3):1179-1195. doi: 10.1007/s10796-022-10271-8. Epub 2022 Apr 30. Inf Syst Front. 2023. PMID: 35529102 Free PMC article.

• U.S. Adult Perspectives on Facial Images, DNA, and Other Biometrics.

  Katsanis SH, Claes P, Doerr M, Cook-Deegan R, Tenenbaum JD, Evans BJ, Lee MK, Anderton J, Weinberg SM, Wagner JK. Katsanis SH, et al. IEEE Trans Technol Soc. 2022 Mar;3(1):9-15. doi: 10.1109/tts.2021.3120317. Epub 2021 Oct 18. IEEE Trans Technol Soc. 2022. PMID: 35360665 Free PMC article.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.3