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Cited by 94Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIRace, Genetics, and Society

Fairness in Precision Medicine

Open access
Ferryman, Kadija·2018
DOI 10.69985/tbql1779
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Abstract

Imagine that in the future, someone seeking medical care meets with a clinician who has a data resource that includes not only her medical history but also her genetic sequence and activity tracker information, as well as data about her housing, water, air quality, and the strength of her social networks.From this data, the clinician would know what diseases she was at risk for before developing any symptoms, and would even know which medications would work best if there was disease onset.For some, this scenario is empowering; for others, it's terrifying.Yet, this is the ideal of precision medicine, an emerging approach that aims to capitalize on the growing availability of health data to both deliver better care to individuals and to improve the efficiency of the health care system as a whole. RESEARCH QUESTIONSThis dream of a precision medicine future contrasts with the reality of medical care in the United States today.The United States spends more money on health care than any other country, but does not correspondingly have the best health outcomes.Furthermore, health disparities among demographic groups are staggering. 1 Access to data, specifically large volumes and varieties of health data, could help health care providers intervene and begin to address some of our health care ills.The rapid digitization of medical records 2 and the advances in computing and data analysis techniques mean that other kinds of data, in addition to genetics, can also be mined for insights about health and disease.3 The hope that new forms of data and information processing could be harnessed to lead to better medicine has been an important impetus of medical research for many years.For example, scientists hoped that sequencing the entire genome in the Human Genome Project would not just advance molecular biology, but would be applied to medicine and lead to exciting and unprecedented insights about health.4 Though we can trace the genealogy of precision medicine from genetic research, another important part of the story is increasing scientific interest and research on other, nongenetic factors for disease.

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  • Bias in healthcare AI is application specific.

    “Bias in healthcare AI is application specific.32”

    From FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2025) · cites this paper as [32]

    The “Fairness in Precision Medicine” section says that bias can arise from human assumptions, automation, and different phases of data handling. That supports the idea that bias depends on how a system is developed and used, but the paper discusses precision medicine rather than establishing the claim for healthcare AI applications generally. No page number is available in our copy. (LLM, conf=0.75)

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Cites

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Research works printed but not linked: 34

  1. J. Larry Jameson and Dan L. Longo, "Precision Medicine: Personalized, Problematic, and Promising," The New England Journal of Medicine; Boston 372, no. 23 (June 4, 2015): 2229-34;
  2. Richard Hodson, "Precision Medicine," Nature 537, no. 7619 (September 8, 2016): S49-S49, https://doi.org/10.1038/537S49a. · DOI
  3. Nancy Krieger, "Stormy Weather: Race, Gene Expression, and the Science of Health Disparities," American Journal of Public Health 95, no. 12 (December 2005): 2155-60, https://doi.org/10.2105/AJPH.2005.067108. · DOI
  4. Virginia Eubanks, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (St. Martin's Press, 2018).
  5. Jean Heller, "Syphilis Victims in U.S. Study Went Untreated for 40 Years," The New York Times, July 26, 1972, sec. Archives, https://www.nytimes.com/1972/ 07/26/archives/syphilis-victims-in-us-study-went-untreated-for-40-years-syphilis. html; Hideyo Noguchi, "Experimental Research in Syphilis: With Especial Reference to Spiroch[Ill]Eta Pallida (Treponema Pallidum)," Journal of the American Medical Association LVIII, no. 16 (April 20, 1912): 1163-72, https://doi.org/10.1001/jama.1912. 04260040179001; · DOI
  6. Rebecca Skloot, The Immortal Life of Henrietta Lacks (Movie Tie-In Edition) (Broadway Books, 2017);
  7. C. P. Blacker, "'Eugenic' Experiments Conducted by the Nazis on Human Subjects," The Eugenics Review 44, no. 1 (April 1952): 9-19.
  8. Seung Ho Shin, Ann M. Bode, and Zigang Dong, "Precision Medicine: The Foundation of Future Cancer Therapeutics," Npj Precision Oncology 1, no. 1 (December 2017): 12, https://doi.org/10.1038/s41698-017-0016-z. · DOI
  9. Marylyn D. Ritchie, "The Success of Pharmacogenomics in Moving Genetic Association Studies from Bench to Bedside: Study Design and Implementation of Precision Medicine in the Post-GWAS Era," Human Genetics 131, no. 10 (2012): 1615-26, https://doi.org/10.1007/s00439-012-1221-z. · DOI
  10. Mark A. Rothstein, "Ethical Issues in Big Data Health Research: Currents in Contemporary Bioethics," The Journal of Law, Medicine & Ethics 43, no. 2 (June 1, 2015): 425-29, https://doi.org/10.1111/jlme.12258. · DOI
  11. danah boyd and Kate Crawford, "Six Provocations for Big Data," SSRN Scholarly Paper (Rochester, NY: Social Science Research Network, September 21, 2011), http://papers.ssrn.com/abstract=1926431. The White House, "Precision Medicine Initiative: Privacy and Trust Principles," November 9, 2015, https://obamawhitehouse.archives.gov/sites/default/files/ microsites/finalpmiprivacyandtrustprinciples.pdf.
  12. Christopher J. Pannucci and Edwin G. Wilkins, "Identifying and Avoiding Bias in Research," Plastic and Reconstructive Surgery 126, no. 2 (August 2010): 619-25, https://doi.org/10.1097/PRS.0b013e3181de24bc. · DOI · Paper page
  13. Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan, "Semantics Derived Automatically from Language Corpora Contain Human-like Biases," Science 356, no. 6334 (April 14, 2017): 183-86, https://doi.org/10.1126/science.aal4230. ENDNOTES See Appendix 1 to view the interview protocol. 26 See Appendix 2 for participant names and affiliations. · DOI
  14. Riccardo Miotto et al., "Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records," Scientific Reports 6 (May 17, 2016): 26094, https://doi.org/10.1038/srep26094; Maya Rotmensch et al., "Learning a Health Knowledge Graph from Electronic Medical Records," Scientific Reports 7, no. 1 (July 20, 2017): 5994, https://doi.org/10.1038/s41598-017-05778-z. · DOI · Paper page
  15. Kathleen Pine and Melissa Mazmanian, "Institutional Logics of the EMR and the Problem of 'perfect' but Inaccurate Accounts" (17th ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2014, Association for Computing Machinery, 2014), https://doi.org/10.1145/2531602.2531652. · DOI
  16. Elizabeth G. Cohn et al., "Distributive Justice, Diversity, and Inclusion in Precision Medicine: What Will Success Look Like?," Genetics in Medicine, August 4, 2016, https://doi.org/10.1038/gim.2016.92. · DOI
  17. See Perry, William R.G., Alvin C. Kwok, Christina Kozycki, and Leo A. Celi. "Disparities in End-of-Life Care: A Perspective and Review of Quality." Population Health Management 16, no. 2 (February 13, 2013): 71-73. https://doi.org/10.1089/ pop.2012.0061, and Ghassemi, Marzyeh, Leo Anthony Celi, and David J. Stone. "State of the Art Review: The Data Revolution in Critical Care." Critical Care 19 (March 16, 2015): 118. https://doi.org/10.1186/s13054-015-0801-4. ENDNOTES · DOI
  18. Mark A. Rothstein, "Structural Challenges of Precision Medicine: Currents in Contemporary Bioethics," Journal of Law, Medicine & Ethics 45, no. 2 (Summer 2017): 274-79, https://doi.org/10.1177/1073110517720655. · DOI
  19. Blacker, C. P. "'Eugenic' Experiments Conducted by the Nazis on Human Subjects." The Eugenics Review 44, no. 1 (April 1952): 9-19.
  20. boyd, danah, and Kate Crawford. "Six Provocations for Big Data." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, September 21, 2011. http://papers.ssrn.com/abstract=1926431.
  21. Caliskan, Aylin, Joanna J. Bryson, and Arvind Narayanan. "Semantics Derived Automatically from Language Corpora Contain Human-like Biases." Science 356, no. 6334 (April 14, 2017): 183-86. https://doi.org/10.1126/science.aal4230. · DOI · Paper page
  22. Cohn, Elizabeth G., Gail E. Henderson, Paul S. Appelbaum, and for the Working Group on Representation and Inclusion in Precision Medicine Studies. "Distributive Justice, Diversity, and Inclusion in Precision Medicine: What Will Success Look Like?" Genetics in Medicine, August 4, 2016. https://doi.org/10.1038/gim.2016. · DOI
  23. Hodson, Richard. "Precision Medicine." Nature 537, no. 7619 (September 8, 2016): S49-S49. https://doi.org/10.1038/537S49a. · DOI
  24. Jameson, J. Larry, and Dan L. Longo. "Precision Medicine: Personalized, Problematic, and Promising." The New England Journal of Medicine; Boston 372, no. 23 (June 4, 2015): 2229-34.
  25. Jensen, Peter B., Lars J. Jensen, and Søren Brunak. "Mining Electronic Health Records: Towards Better Research Applications and Clinical Care." Nature Reviews Genetics 13, no. 6 (June 2012): 395. https://doi.org/10.1038/nrg3208. · DOI · Paper page
  26. Krieger, Nancy. "Stormy Weather: Race, Gene Expression, and the Science of Health Disparities." American Journal of Public Health 95, no. 12 (December 2005): 2155-60. https://doi.org/10.2105/AJPH.2005.067108. · DOI
  27. Miotto, Riccardo, Li Li, Brian A. Kidd, and Joel T. Dudley. "Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records." Scientific Reports 6 (May 17, 2016): 26094. https://doi.org/10.1038/srep26094. · DOI · Paper page
  28. Noguchi, Hideyo. "Experimental Research in Syphilis: With Especial Reference to Spiroch[Ill] Eta Pallida (Treponema Pallidum)." Journal of the American Medical Association LVIII, no. 16 (April 20, 1912): 1163-72. https://doi.org/10.1001/jama.1912.04260040179001. · DOI
  29. Pannucci, Christopher J., and Edwin G. Wilkins. "Identifying and Avoiding Bias in Research." Plastic and Reconstructive Surgery 126, no. 2 (August 2010): 619-25. https://doi.org/10.1097/PRS.0b013e3181de24bc. REFERENCES FAIRNESS IN PRECISION MEDICINE Pine, Kathleen, and Melissa Mazmanian. "Institutional Logics of the EMR and the Problem of 'Perfect' but Inaccurate Accounts." Association for Computing Machinery, 2014. https://doi.org/10.1145/2531602.2531652. · DOI · Paper page
  30. Ritchie, Marylyn D. "The Success of Pharmacogenomics in Moving Genetic Association Studies from Bench to Bedside: Study Design and Implementation of Precision Medicine in the Post-GWAS Era." Human Genetics 131, no. 10 (2012): 1615-26. https://doi.org/10.1007/s00439-012-1221-z. · DOI
  31. Rothstein, Mark A. "Ethical Issues in Big Data Health Research: Currents in Contemporary Bioethics." The Journal of Law, Medicine & Ethics 43, no. 2 (June 1, 2015): 425-29. https://doi.org/10.1111/jlme.12258. "Structural Challenges of Precision Medicine: Currents in Contemporary Bioethics." Journal of Law, Medicine & Ethics 45, no. 2 (Summer 2017): 274-79. https://doi.org/10.1177/1073110517720655. · DOI
  32. Rotmensch, Maya, Yoni Halpern, Abdulhakim Tlimat, Steven Horng, and David Sontag. "Learning a Health Knowledge Graph from Electronic Medical Records." Scientific Reports 7, no. 1 (July 20, 2017): 5994. https://doi.org/10.1038/s41598-017-05778-z. · DOI · Paper page
  33. Shin, Seung Ho, Ann M. Bode, and Zigang Dong. "Precision Medicine: The Foundation of Future Cancer Therapeutics." Npj Precision Oncology, no. 1 (December 2017): 12. https://doi.org/10.1038/s41698-017-0016-z. · DOI
  34. Skloot, Rebecca. The Immortal Life of Henrietta Lacks (Movie Tie-In Edition). New York: Broadway Books, 2017.

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Authors
Ferryman, K.
Corresponding author
Ferryman, Kadija
ORCID
Ferryman, Kadija
Publisher
Data & Society Research Institute
Published
2018-02-26
DOI
10.69985/tbql1779
OpenAlex
W2890209530
Cited by
94
References
1
Citations by year
2026: 7 · 2025: 3 · 2024: 7 · 2023: 22 · 2022: 14 · 2021: 13 · 2020: 14 · 2019: 9 · 2018: 5
Type
report
Language
en
Open access
Licence
cc-by-nc-sa
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
Keywords
precision medicine, fairness, health disparities, health data, health equity, digital health data, genetic data, social determinants of health, algorithmic bias, health care access

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