Have a personal or library account? Click to login
Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach Cover

Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach

Annals of Global Health
Volume 87 (2021): Issue 1
By: Jerry M. Spiegel,  Rodney Ehrlich,  Annalee Yassi,  Francisco Riera,  James Wilkinson,  Karen Lockhart,  Stephen Barker and  Barry Kistnasamy  
Open Access
|Jul 2021

References

  1. 1Helbing D, Frey BS, Gigerenzer G, et al. Will democracy survive big data and artificial intelligence? Towards digital enlightenment. Springer. 2019; 73–98. DOI: 10.1007/978-3-319-90869-4_7
    Back to article
  2. 2Naudé W. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI & Society. 2020; 35: 761–765. DOI: 10.1007/s00146-020-00978-0
    Back to article
  3. 3Buchanan BG. A (very) brief history of artificial intelligence. AI Magazine. 2005; 26(4): 53–53.
    Back to article
  4. 4Danneels E. Disruptive technology reconsidered: A critique and research agenda. Journal of Product Innovation Management. 2004; 21(4): 246–258. DOI: 10.1111/j.0737-6782.2004.00076.x
    Back to article
  5. 5Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B. Interpretable machine learning: Definitions, methods, and applications. arXiv preprint arXiv:190104592. 2019.
    Back to article
  6. 6Murray NM, Unberath M, Hager GD, Hui FK. Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: A systematic review. Journal of Neurointerventional Surgery. 2020; 12(2): 156–164. DOI: 10.1136/neurintsurg-2019-015135
    Back to article
  7. 7Carter SM, Rogers W, Win KT, Frazer H, Richards B, Houssami N. The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. The Breast. 2020; 49: 25–32. DOI: 10.1016/j.breast.2019.10.001
    Back to article
  8. 8Marka A, Carter JB, Toto E, Hassanpour S. Automated detection of nonmelanoma skin cancer using digital images: A systematic review. BMC Medical Imaging. 2019; 19(1): 21. DOI: 10.1186/s12880-019-0307-7
    Back to article
  9. 9Dubey AK, Gupta U, Jain S. Epidemiology of lung cancer and approaches for its prediction: A systematic review and analysis. Chinese Journal of Cancer. 2016; 35(1): 71. DOI: 10.1186/s40880-016-0135-x
    Back to article
  10. 10Université de Montréal. Montreal Declaration for Responsible AI. https://www.montrealdeclaration-responsibleai.com/the-declaration. Accessed: 2 March 2020; 2017.
    Back to article
  11. 11Floridi L, Cowls J, Beltrametti M, et al. AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines. 2018; 28(4): 689–707. DOI: 10.1007/s11023-018-9482-5
    Back to article
  12. 12Chui M, Harrysson M, Manyika J, et al. Applying Artificial Intelligence for Social Good. https://www.mckinsey.com/featured-insights/artificial-intelligence/applying-artificial-intelligence-for-social-good#: McKinsey & Company; December 2018.
    Back to article
  13. 13Zumla A, Petersen E. The historic and unprecedented United Nations General Assembly High Level Meeting on Tuberculosis (UNGA-HLM-TB)—‘United to End TB: An Urgent Global Response to a Global Epidemic’. International Journal of Infectious Diseases. 2018; 75: 118–120. DOI: 10.1016/j.ijid.2018.09.017
    Back to article
  14. 14Shiraishi J, Li Q, Appelbaum D, Doi K. Computer-aided diagnosis and artificial intelligence in clinical imaging. Seminars in Nuclear Medicine. 2011; 41(6): 449–462. DOI: 10.1053/j.semnuclmed.2011.06.004
    Back to article
  15. 15Liang C-H, Liu Y-C, Wu M-T, Garcia-Castro F, Alberich-Bayarri A, Wu F-Z. Identifying pulmonary nodules or masses on chest radiography using deep learning: External validation and strategies to improve clinical practice. Clinical Radiology. 2020; 75(1): 38–45. DOI: 10.1016/j.crad.2019.08.005
    Back to article
  16. 16Maduskar P, Muyoyeta M, Ayles H, Hogeweg L, Peters-Bax L, van Ginneken B. Detection of tuberculosis using digital chest radiography: Automated reading vs. interpretation by clinical officers. The International Journal of Tuberculosis and Lung Disease. 2013; 17(12): 1613–1620. DOI: 10.5588/ijtld.13.0325
    Back to article
  17. 17Harris M, Qi A, Jeagal L, et al. A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis. PloS One. 2019; 14(9). DOI: 10.1371/journal.pone.0221339
    Back to article
  18. 18Park HH, Girdler-Brown BV, Churchyard GJ, White NW, Ehrlich RI. Incidence of tuberculosis and HIV and progression of silicosis and lung function impairment among former Basotho gold miners. American Journal of Industrial Medicine. 2009; 52(12): 901–908. DOI: 10.1002/ajim.20767
    Back to article
  19. 19Murray J, Davies T, Rees D. Occupational lung disease in the South African mining industry: research and policy implementation. Journal of Public Health Policy. 2011; 32(1): S65–S79. DOI: 10.1057/jphp.2011.25
    Back to article
  20. 20Stuckler D, Steele S, Lurie M, Basu S. Introduction: ‘Dying for gold’: The effects of mineral mining on HIV, tuberculosis, silicosis, and occupational diseases in Southern Africa. International Journal of Health Services. 2013; 43(4): 639–649. DOI: 10.2190/HS.43.4.c
    Back to article
  21. 21Rees D, Murray J, Nelson G, Sonnenberg P. Oscillating migration and the epidemics of silicosis, tuberculosis, and HIV infection in South African gold miners. American Journal of Industrial Medicine. 2010; 53(4): 398–404. DOI: 10.1002/ajim.20716
    Back to article
  22. 22Smith J, Blom P. Those who don’t return: improving efforts to address tuberculosis among former miners in Southern Africa. New Solutions: A Journal of Environmental and Occupational Health Policy. 2019; 29(1): 76–104. DOI: 10.1177/1048291119832082
    Back to article
  23. 23Ehrlich R, Akugizibwe P, Siegfried N, Rees D. Silica exposure, silicosis and tuberculosis – a systematic review. BMC Public Health. 2021 in press. DOI: 10.1186/s12889-021-10711-1
    Back to article
  24. 24Hnizdo E, Murray J. Risk of pulmonary tuberculosis relative to silicosis and exposure to silica dust in South African gold miners. Occupational and Environmental Medicine. 1998; 55(7): 496–502. DOI: 10.1136/oem.55.7.496
    Back to article
  25. 25Solomon A. Silicosis and tuberculosis: Part 2—a radiographic presentation of nodular tuberculosis and silicosis. International Journal of Occupational and Environmental Health. 2001; 7(1): 54–57. DOI: 10.1179/oeh.2001.7.1.54
    Back to article
  26. 26Stuckler D, Basu S, McKee M, Lurie M. Mining and risk of tuberculosis in sub-Saharan Africa. American Journal of Public Health. 2011; 101(3): 524–530. DOI: 10.2105/AJPH.2009.175646
    Back to article
  27. 27Ehrlich R. A century of miners’ compensation in South Africa. American Journal of Industrial Medicine. 2012; 55(6): 560–569. DOI: 10.1002/ajim.22030
    Back to article
  28. 28Maboso B, Moyo D, Muteba K, et al. Burden of disease among Basotho ex-miners in a large out-reach medical assessment programme. Occupational Health Southern Africa. 2020; 26(4): 145–152.
    Back to article
  29. 29Kistnasamy B, Yassi A, Yu J, et al. Tackling injustices of occupational lung disease acquired in South African mines: Recent developments and ongoing challenges. Globalization and Health. 2018; 14(60). DOI: 10.1186/s12992-018-0399-9
    Back to article
  30. 30Tshiamiso Trust. Tshiamiso Trust. https://www.tshiamisotrust.com2021. Accessed March 5, 2021.
    Back to article
  31. 31Young C, Barker S, Ehrlich R, Kistnasamy B, Yassi A. Computer-aided detection for tuberculosis and silicosis in chest radiographs of gold miners of South Africa. International Journal of TB and Lung Diseases. 2020; 24: 444–451. DOI: 10.5588/ijtld.19.0624
    Back to article
  32. 32Laney A, Pontali E. Computer-assisted interpretation of chest radiographs: Signs of hope for silicosis and tuberculosis. The International Journal of Tuberculosis and Lung Disease. 2020; 24(4): 362–363. DOI: 10.5588/ijtld.19.0805
    Back to article
  33. 33Yassi A, Spiegel J, Barker S. Improving efficiency of assessing (ex)miners for tuberculosis (TB) and silicosis: Innovations to promote social justice. http://med-fom-ghrp-spph.sites.olt.ubc.ca/files/2021/05/May-5th-2021-ayFinal.pdf. Accessed May 5, 2021.
    Back to article
  34. 34Liu V, Musen MA, Chou T. Data breaches of protected health information in the United States. JAMA: The Journal of the American Medical Association. 2015; 313(14): 1471–1473. DOI: 10.1001/jama.2015.2252
    Back to article
  35. 35Canadian Association of Radiologists Artificial Intelligence Working Group. Canadian Association of Radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Canadian Association of Radiologists’ Journal. 2019; 70(2): 107–118. DOI: 10.1016/j.carj.2019.03.001
    Back to article
  36. 36Beauchamp T, Childress J. Principles of Biomedical Ethics, 7th Edition. New York, NY: Oxford University Press; 2013.
    Back to article
  37. 37Koplan JP, Bond TC, Merson MH, et al. Towards a common definition of global health. The Lancet. 2009; 373(9679): 1993–1995. DOI: 10.1016/S0140-6736(09)60332-9
    Back to article
  38. 38Yassi A, Spiegel J, Barker S, Ehrlich R. Use of computer aided detection to support triage for efficiency at the MBOD. http://med-fom-ghrp-spph.sites.olt.ubc.ca/files/2021/05/Rodney-presentation-mining-may5.pdf. Accessed May 5, 2021.
    Back to article
  39. 39Khan FA, Pande T, Tessema B, et al. Computer-aided reading of tuberculosis chest radiography: Moving the research agenda forward to inform policy. European Respiratory Journal. 2017; 50(1700953). DOI: 10.1183/13993003.00953-2017
    Back to article
  40. 40Franzblau A, teWaterNaude J, Sen A, et al. Comparison of digital and film chest radiography for detection and medical surveillance of silicosis in a setting with a high burden of tuberculosis. American Journal of Industrial Medicine. 2018; 61(3): 229–238. DOI: 10.1002/ajim.22803
    Back to article
  41. 41Girdler-Brown BV, White NW, Ehrlich RI, Churchyard GJ. The burden of silicosis, pulmonary tuberculosis and COPD among former Basotho goldminers. American Journal of Industrial Medicine. 2008; 51(9): 640–647. DOI: 10.1002/ajim.20602
    Back to article
  42. 42Winfield AF, Jirotka M. Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2018; 376(2133). DOI: 10.1098/rsta.2018.0085
    Back to article
  43. 43Republic of South Africa. Protection of Personal Information Act (POPI Act). https://popiacoza/2019-2021. Accessed May 5, 2021.
    Back to article
  44. 44Dwivedi YK, Hughes L, Ismagilova E, et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management. 2019; 101994. DOI: 10.1016/j.ijinfomgt.2019.08.002
    Back to article
  45. 45Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine. 2019; 25(1): 44. DOI: 10.1038/s41591-018-0300-7
    Back to article
  46. 46Coiera E. The fate of medicine in the time of AI. The Lancet. 2018; 392(10162): 2331–2332. DOI: 10.1016/S0140-6736(18)31925-1
    Back to article
  47. 47Canadian Institutes of Health Research (CIHR), Canadian Institute for Advanced Research (CIFAR). AI for Public Health Equity. January 25, 2019.
    Back to article
  48. 48Sun TQ, Medaglia R. Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly. 2019; 36(2): 368–383. DOI: 10.1016/j.giq.2018.09.008
    Back to article
  49. 49Lewis P. Sick miners to get up to R500k: Historic settlement reached in silicosis case. GroundUp. May 3, 2018. https://www.groundup.org.za/article/historic-settlement-between-gold-industry-and-ex-mineworkers-lung-disease/.
    Back to article
  50. 50Mahajan A, Vaidya T, Gupta A, Rane S, Gupta S. Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey. Cancer Research, Statistics, and Treatment. 2019; 2(2): 182. DOI: 10.4103/CRST.CRST_50_19
    Back to article
  51. 51Treviranus J. The three dimensions of inclusive design: A design framework for a digitally transformed and complexly connected society. University College Dublin; 2018.
    Back to article
  52. 52Racine E, Boehlen W, Sample M. Healthcare uses of artificial intelligence: Challenges and opportunities for growth Healthcare Management Forum 2019. Los Angeles, CA: SAGE Publications. DOI: 10.1177/0840470419843831
    Back to article
  53. 53Loh E. Medicine and the rise of the robots: A qualitative review of recent advances of artificial intelligence in health. BMJ Leader. 2018. DOI: 10.1136/leader-2018-000071
    Back to article
  54. 54Courtland R. Bias detectives: The researchers striving to make algorithms fair. Nature. 2018; 558(7710): 357–357. DOI: 10.1038/d41586-018-05469-3
    Back to article
  55. 55Campbell SH, Scott JT. Rousseau’s Politic Argument in the Discourse on the Sciences and Arts. American Journal of Political Science. 2005; 49(4): 818–828. DOI: 10.1111/j.1540-5907.2005.00157.x
    Back to article
  56. 56Horton R. Offline: The case against (and for) public health. The Lancet. 2016; 388(10060): 2578. DOI: 10.1016/S0140-6736(16)32387-X
    Back to article
DOI: https://doi.org/10.5334/aogh.3206 | Journal eISSN: 2214-9996
Journal RSS Feed
Language: English
Published on: Jul 1, 2021
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2021 Jerry M. Spiegel, Rodney Ehrlich, Annalee Yassi, Francisco Riera, James Wilkinson, Karen Lockhart, Stephen Barker, Barry Kistnasamy, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 87 (2021): Issue 1Next article