Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-22T16:49:15.242Z Has data issue: false hasContentIssue false

Artificial Intelligence in Global Health

Published online by Cambridge University Press:  07 June 2019

Abstract

Artificial intelligence (AI) is reaching into every aspect of global health. In this essay, I examine one example of AI's potential contributions and limitations in global health: the prediction, treatment, and containment of a global influenza outbreak. The potential advantages are clear. AI can aid global influenza surveillance platforms by improving the capacity of organizations to look for novel influenza outbreak strains in the right places, to identify populations most likely to spread influenza, and to produce real-time information about the disease's spread by monitoring social media communications to track outbreak events. There are also very real limitations to what AI can do, and it is crucial that AI not be used as an excuse not to invest in strengthening health systems and other traditional components of global healthcare. AI may also be able to improve our understanding of who should receive a vaccine and what is most effective for large-scale vaccine delivery, but there will always be blind spots that the data cannot fill. Investment in healthcare, with attention to the danger of minimal access to care for minority groups that are at risk and in fragile situations, remains the best chance to prepare communities for outbreak detection, surveillance, and containment.

Type
Roundtable: Artificial Intelligence and the Future of Global Affairs
Copyright
Copyright © Carnegie Council for Ethics in International Affairs 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

NOTES

1 “Artificial Intelligence,” ITU, www.itu.int/en/ITU-T/AI/Pages/default.aspx.

2 “Terms of Reference: ITU-T Focus Group on ‘Artificial Intelligence for Health’ (FG-AI4H),” ITU, July 20, 2018, www.itu.int/en/ITU-T/focusgroups/ai4h/Documents/FG-AI4H-ToR.pdf. See also Marcel Salathé, Thomas Wiegand, Markus Wenzel, and Ramesh Kishnamurthy, Focus Group on Artificial Intelligence for Health, www.itu.int/en/ITU-T/focusgroups/ai4h/Documents/FG-AI4H_Whitepaper.pdf.

3 “Terms of Reference,” ITU, p. 2.

4 The End TB Strategy (Geneva: World Health Organization, 2015), p. 3Google Scholar, www.who.int/tb/End_TB_brochure.pdf; Jaeger, Stefan, Juarez-Espinosa, Octavio H., Candemir, Sema, Poostchi, Mandieh, Yang, Feng, Kim, Lewis, Ding, Meng, et al. , “Detecting Drug-Resistant Tuberculosis in Chest Radiographs,” International Journal of Computer Assisted Radiology and Surgery 13, no. 12 (October 2018), pp. 1915–25CrossRefGoogle ScholarPubMed.

5 Hay, Simon I., George, Dylan B., Moyes, Catherine L., and Brownstein, John S., “Big Data Opportunities for Global Infectious Disease Surveillance,” PLoS Medicine 10, no. 4 (April 2013)CrossRefGoogle ScholarPubMed; Elbe, Stefan and Buckland-Merrett, Gemma, “Data, Disease and Diplomacy: GISAID's Innovative Contribution to Global Health,” Global Challenges 1, no. 1 (January 2017), pp. 3346CrossRefGoogle Scholar; Vayena, Effy, Dzenowagis, Joan, Brownstein, John S., and Sheikh, Aziz, “Policy Implications of Big Data in the Health Sector,” Bulletin of the World Health Organization 96, no. 1 (November 2017), pp. 6668CrossRefGoogle ScholarPubMed.

6 “Big Data and Artificial Intelligence for Achieving Universal Health Coverage: An International Consultation on Ethics” (meeting report, World Health Organization, Miami, October 12–13, 2017), p. vi, apps.who.int/iris/bitstream/handle/10665/275417/WHO-HMM-IER-REK-2018.2-eng.pdf?ua=1.

7 Simon B. Johnson, “A Basis for an Ethical AI Framework for Humanitarian Response,” Medium, November 26, 2018, medium.com/@Simon_B_Johnson/a-basis-of-an-ethical-ai-framework-for-humanitarian-response-bc2938b99f80; Babusi Nyoni, “How Artificial Intelligence Can Be Used to Predict Africa's Next Migration Crisis,” UNHCR, February 10, 2017, www.unhcr.org/innovation/how-artificial-intelligence-can-be-used-to-predict-africas-next-migration-crisis/.

8 Mark Latonero, Governing Artificial Intelligence: Upholding Human Rights & Dignity, Data & Society, October 10, 2018, p. 19, datasociety.net/output/governing-artificial-intelligence/.

9 “History of 1918 Flu Pandemic,” Centers for Disease Control and Prevention, March 21, 2018, www.cdc.gov/flu/pandemic-resources/1918-commemoration/1918-pandemic-history.htm.

10 Mark Eccleston-Turner, “Another Major Flu Pandemic Will Happen. We're Not Ready,” World Economic Forum, September 11, 2018, www.weforum.org/agenda/2018/09/flu-plane-are-we-really-ready-for-a-global-pandemic; Mark Honigsbaum, “Spanish Flu: The Killer That Still Stalks Us, 100 Years On,” Guardian, September 9, 2018, www.theguardian.com/world/2018/sep/09/spanish-flu-pandemic-centenary-first-world-war.

11 Clare Wenham et al., “Self-Swabbing for Virological Confirmation of Influenza-Like Illness Among an Internet-Based Cohort in the UK during the 2014–2015 Flu Season: Pilot Study,” Journal of Medical Internet Research 20, no. 3 (March 2018), p. e71.

12 Chen, Jonathan H. and Asch, Steven M., “Machine Learning and Prediction in Medicine–Beyond the Peak of Inflated Expectations,” New England Journal of Medicine 376 (June 2017), pp. 2507–9CrossRefGoogle ScholarPubMed.

13 Michael J. Paul, Mark Dredze, David A. Broniatowski, and Nicholas Generous, “Worldwide Influenza Surveillance through Twitter,” in World Wide Web and Public Health Intelligence: Papers from the 2015 AAAI Workshop, AAAI, 2015, www.aaai.org/ocs/index.php/WS/AAAIW15/paper/viewFile/10161/10255; Spreco, Armin, Eriksson, Olle, Dahlström, Örjan, Cowling, Benjamin John, and Timpka, Toomas, “Evaluation of Nowcasting for Detecting and Predicting Local Influenza Epidemics, Sweden, 2009–2014,” Emerging Infectious Diseases 24, no. 10 (October 2018), pp. 1868–73CrossRefGoogle Scholar.

14 “Big Data and Artificial Intelligence for Achieving Universal Health Coverage,” p. vii.

15 Hirve, Siddhivinayak, Seasonal Influenza Vaccine Use in Low and Middle Income Countries in the Tropics and Subtropics: A Systematic Review (Geneva: World Health Organization, 2015), p. 25Google Scholar.

16 Polansky, Lauren S., Outin-Blenman, Sajata, and Moen, Ann C., “Improved Global Capacity for Influenza Surveillance,” Emerging Infectious Diseases 22, no. 6 (June 2016), pp. 9931001CrossRefGoogle ScholarPubMed; Justin R. Ortiz and Kathleen M. Neuzil, “Influenza Immunization in Low- and Middle-Income Countries: Preparing for Next-Generation Influenza Vaccines,” Journal of Infectious Diseases (January 2019).

17 Hirve, Seasonal Influenza Vaccine Use in Low and Middle Income Countries, p. 27.

18 Zhang, Wenqing, Hirve, Siddhivinayak, and Kieny, Marie-Paule, “Seasonal Vaccines—Critical Path to Pandemic Influenza Response,” Vaccine 35, no. 6 (January 2017), p. 852CrossRefGoogle ScholarPubMed.

19 Zou, James and Schiebinger, Londa, “AI Can Be Sexist and Racist—It's Time to Make It Fair,” Nature 559 (July 2018), pp. 324–26CrossRefGoogle ScholarPubMed.

20 Bresee, Joseph, Fitzner, Julia, Campbell, Harry, Cohen, Cheryl, Cozza, Vanessa, Jara, Jorge, Krishnan, Anand, Lee, Vernon, and for the WHO Working Group on the Burden of Influenza Disease, “Progress and Remaining Gaps in Estimating the Global Disease Burden of Influenza,” Emerging Infectious Diseases 24, no. 7 (July 2018), p. 1174CrossRefGoogle ScholarPubMed.

21 Organization, World Health, Global Pandemic Influenza Action Plan to Increase Vaccine Supply (Geneva: World Health Organization, 2006), p.viGoogle Scholar.

22 Bresee et al., “Progress and Remaining Gaps in Estimating the Global Disease Burden of Influenza,” pp. 1173–77.

23 World Health Organization, 2018 Assessment Report of the Global Vaccine Action Plan: Strategic Advisory Group of Experts on Immunization (Geneva: World Health Organization, 2018), p. 17Google Scholar.

24 Hirve, Seasonal Influenza Vaccine Use in Low and Middle Income Countries, pp. 16–25, pp. 35–36.

25 Elbe and Buckland-Merrett, “Data, Disease and Diplomacy.”

26 Elbe, Stefan, Roemer-Mahler, Anne, and Long, Christopher, “Medical Countermeasures for National Security: A New Government Role in the Pharmaceuticalization of Society,” Social Science and Medicine 131 (April 2015), pp. 263–71CrossRefGoogle Scholar.

27 Bresee et al., “Progress and Remaining Gaps in Estimating the Global Disease Burden of Influenza,” p. 1175.

28 Debellut, Frédéric, Hendrix, Nathaniel, Ortiz, Justin R., Neuzil, Kathleen M., Bhat, Niranjan, and Pecenka, Clint, “Forecasting Demand for Maternal Influenza Immunization in Low- and Lower-Middle-Income Countries,” PLoS ONE 13, no. 6 (2018), p. e0199470CrossRefGoogle ScholarPubMed.

29 Hirve, Seasonal Influenza Vaccine Use in Low and Middle Income Countries, pp. 16–25, pp. 35–36; Zhang, Hirve, and Kieny, “Seasonal Vaccines,” p. 852. In one study, even though the influenza vaccination against H1N1 was free and compulsory for healthcare workers, they did not always take the vaccination because of fears of adverse side effects. See Purohit, Vidula, Kudale, Abhay, Sundaram, Neisha, Joseph, Saju, Schaetti, Christian, and Weiss, Mitchell, “Public Health Policy and Experience of the 2009 H1N1 Influenza Pandemic in Pune, India,” International Journal of Health Policy and Management 7, no. 2 (February 2018), pp. 154–66CrossRefGoogle Scholar.

30 Dan McQuillan, “AI Will Be Used by Humanitarian Organisations—This Could Deepen Neocolonial Tendencies,” Conversation, April 23, 2018, theconversation.com/ai-will-be-used-by-humanitarian-organisations-this-could-deepen-neocolonial-tendencies-92547.

31 Nicholas D. Wright, ed., AI, China, Russia, and the Global Order: Technological, Political, Global, and Creative Perspectives, NSI, December 2018, nsiteam.com/social/wp-content/uploads/2018/12/AI-China-Russia-Global-WP_FINAL.pdf.

32 Zidar, Andraž, “WHO International Health Regulations and Human Rights: From Allusions to Inclusion,” International Journal of Human Rights 19, no. 4 (June 2015), pp. 505–26CrossRefGoogle Scholar.

33 Eccleston-Turner, Mark, “Operationalizing the Right to Health through the Pandemic Influenza Preparedness Framework,” special issue, Global Health Governance 12, no. 1, pp. 2233Google Scholar.

34 “Big Data and Artificial Intelligence for Achieving Universal Health Coverage.”.

35 See n. 2.

36 ITU-T Focus Group on AI for Health, “Updated Call for Proposals: Use Cases, Benchmarking, and Data,” ITU, November 15–16, 2018, www.itu.int/en/ITU-T/focusgroups/ai4h/Documents/FGAI4H-B-102-DraftCfP_UC_Benchm_Data.pdf.

37 Brandie Nonnecke, “Artificial Intelligence Can Make Our Societies More Equal. Here's How,” World Economic Forum, September 21, 2017, www.weforum.org/agenda/2017/09/applying-ai-to-enable-an-equitable-digital-economy-and-society/.

38 “Third ITU/WHO Workshop on ‘Artificial Intelligence for Health,’ Lausanne, Switzerland, 22 January 2019,” ITU, www.itu.int/en/ITU-T/Workshops-and-Seminars/ai4h/20190122/Pages/programme.aspx.