Atkinson, R. D. & Castro, D. Digital Quality of Life: Understanding the Personal and Social Benefits of the Information Technology Revolution. https://papers.ssrn.com/abstract=1278185 (2008).
Murdoch, T. B. & Detsky, A. S. The inevitable application of big data to health care. JAMA 309, 1351–1352 (2013).
Topol, E. The Creative Destruction Of Medicine: How The Digital Revolution Will Create Better Health Care (Basic Books, 2012).
Ceruzzi, P. E. Computing: A Concise History. (MIT Press, 2012).
Wang, P. On defining artificial intelligence. J. Artif. Gen. Intell. 10, 1–37 (2019).
Russell, S. & Norvig, P. Artificial Intelligence: A Modern Approach (Prentice Hall, 2002).
Davenport, T. & Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthc. J. 6, 94–98 (2019).
Liang, H. et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat. Med. 25, 433–438 (2019).
Steinhubl, S. R., Muse, E. D. & Topol, E. J. The emerging field of mobile health. Sci. Transl. Med. 7, 283rv3 (2015).
Goldhahn, J., Rampton, V. & Spinas, G. A. Could artificial intelligence make doctors obsolete? BMJ 363, k4563 (2018).
Reddy, C. L., Mitra, S., Meara, J. G., Atun, R. & Afshar, S. Artificial Intelligence and its role in surgical care in low-income and middle-income countries. Lancet Digit. Health 1, e384–e386 (2019).
Frenk, J. et al. Health professionals for a new century: transforming education to strengthen health systems in an interdependent world. Lancet 376, 1923–1958 (2010).
Oren, O., Gersh, B. J. & Bhatt, D. L. Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit. Health 2, e486–e488 (2020).
Lee, J. et al. Interventions to improve district-level routine health data in low-income and middle-income countries: a systematic review. BMJ Glob. Health 6, e004223 (2021).
Ugarte-Gil, C. et al. Implementing a socio-technical system for computer-aided tuberculosis diagnosis in Peru: A field trial among health professionals in resource-constraint settings. Health Inform. J. 26, 2762–2775 (2020).
Ganju, A., Satyan, S., Tanna, V. & Menezes, S. R. AI for improving children’s health: a community case study. Front. Artif. Intell. 3, 544972 (2021).
Love, S. M. et al. Palpable breast lump triage by minimally trained operators in mexico using computer-assisted diagnosis and low-cost ultrasound. J. Glob. Oncol. https://doi.org/10.1200/JGO.17.00222 (2018).
Garzon-Chavez, D. et al. Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients. PLoS ONE 16, e0251295 (2021).
MacPherson, P. et al. Computer-aided X-ray screening for tuberculosis and HIV testing among adults with cough in Malawi (the PROSPECT study): A randomised trial and cost-effectiveness analysis. PLoS Med. 18, e1003752 (2021).
Zhou, N. et al. Concordance study between IBM watson for oncology and clinical practice for patients with cancer in China. Oncologist 24, 812–819 (2019).
Kisling, K. et al. Fully automatic treatment planning for external-beam radiation therapy of locally advanced cervical cancer: a tool for low-resource clinics. J. Glob. Oncol. https://doi.org/10.1200/JGO.18.00107 (2019).
Wang, D. et al. “Brilliant AI Doctor” in rural clinics: challenges in AI-powered clinical decision support system deployment. in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems 1–18 (ACM, 2021).
Fan, X. et al. Utilization of self-diagnosis health chatbots in real-world settings: case study. J. Med. Internet Res. 23, e19928 (2021).
Wang, L. et al. CASS: towards building a social-support chatbot for online health community. Proc. ACM Hum.-Comput. Interact. 5, 1-31 (2021).
Bumrungrad International Hospital. IBM Watson for Oncology Demo. https://www.youtube.com/watch?v=338CIHlVi7A (2015).
Guo, Y., Hao, Z., Zhao, S., Gong, J. & Yang, F. Artificial intelligence in health care: bibliometric analysis. J. Med. Internet Res. 22, e18228 (2020).
Lu, W. et al. Applications of artificial intelligence in ophthalmology: general overview. J. Ophthalmol. 2018, 5278196 (2018).
Amisha, Malik, P., Pathania, M. & Rathaur, V. K. Overview of artificial intelligence in medicine. J. Fam. Med. Prim. Care 8, 2328–2331 (2019).
Roberts, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021).
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019).
Mathenge, W. et al. Impact of artificial intelligence assessment of diabetic retinopathy on referral service uptake in a low resource setting: The RAIDERS randomized trial. Ophthalmol. Sci. 2, 100168 (2022).
Ruamviboonsuk, P. et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit. Health 4, e235–e244 (2022).
Mhlanga, M., Cimini, T., Amaechi, M., Nwaogwugwu, C. & McGahan, A. From A to O-Positive: Blood Delivery Via Drones in Rwanda. Reach Alliance https://reachalliance.org/wp-content/uploads/2021/03/Zipline-Rwanda-Final-April19.pdf (2021).
Liu, X. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1, e271–e297 (2019).
Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 368, m689 (2020).
Žliobaitė, I., Pechenizkiy, M. & Gama, J. An overview of concept drift applications. in Big Data Analysis: New Algorithms for a New Society (eds. Japkowicz, N. & Stefanowski, J.) 91–114 (Springer International Publishing, 2016).
5 Ways to Deal with the Lack of Data in Machine Learning. KDnuggets. https://www.kdnuggets.com/5-ways-to-deal-with-the-lack-of-data-in-machine-learning.html/.
GIZ. From Strategy To Implementation – On The Pathways Of The Youngest Countries In Sub-saharan Africa Towards Digital Transformation Of Health Systems. https://www.governinghealthfutures2030.org/pdf/resources/FromStrategyToImplementation-GIZReport.pdf (2021).
Nutley, T. & Reynolds, H. Improving the use of health data for health system strengthening. Glob. Health Action 6, 20001 (2013).
Ye, Y., Wamukoya, M., Ezeh, A., Emina, J. B. O. & Sankoh, O. Health and demographic surveillance systems: a step towards full civil registration and vital statistics system in sub-Sahara Africa? BMC Public Health 12, 741 (2012).
Coiera, E. The last mile: where artificial intelligence meets reality. J. Med. Internet Res. 21, e16323 (2019).
Cabitza, F., Campagner, A. & Balsano, C. Bridging the “last mile” gap between AI implementation and operation: “data awareness” that matters. Ann. Transl. Med. 8, 501 (2020).
Asan, O. & Choudhury, A. Research trends in artificial intelligence applications in human factors health care: mapping review. JMIR Hum. Factors 8, e28236 (2021).
Wallis, L., Blessing, P., Dalwai, M. & Shin, S. D. Integrating mHealth at point of care in low- and middle-income settings: the system perspective. Glob. Health Action 10, 1327686 (2017).
Hengstler, M., Enkel, E. & Duelli, S. Applied artificial intelligence and trust—the case of autonomous vehicles and medical assistance devices. Technol. Forecast. Soc. Change 105, 105–120 (2016).
Nundy, S., Montgomery, T. & Wachter, R. M. Promoting trust between patients and physicians in the era of artificial intelligence. JAMA 322, 497–498 (2019).
Gafni, R. & Pavel, T. Cyberattacks against the health-care sectors during the COVID-19 pandemic. Inf. Comput. Secur. 30, 137–150 (2021).
Venkatesh, V. & Bala, H. Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39, 273–315 (2008).
Wolff, J., Pauling, J., Keck, A. & Baumbach, J. The economic impact of artificial intelligence in health care: systematic review. J. Med. Internet Res. 22, e16866 (2020).
Sanyal, C., Stolee, P., Juzwishin, D. & Husereau, D. Economic evaluations of eHealth technologies: a systematic review. PLoS ONE 13, e0198112 (2018).
Chawla, S. et al. Electricity and generator availability in LMIC hospitals: improving access to safe surgery. J. Surg. Res. 223, 136–141 (2018).
Antwi, W. K., Akudjedu, T. N. & Botwe, B. O. Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives. Insights Imaging 12, 80 (2021).
Ng, M. et al. Effective coverage: a metric for monitoring universal health coverage. PLoS Med. 11, e1001730 (2014).
Munn, Z. et al. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med. Res. Methodol. 18, 143 (2018).
Arksey, H. & O’Malley, L. Scoping studies: towards a methodological framework. Int. J. Soc. Res. Methodol. 8, 19–32 (2005).
Peters, M. D. J. et al. Guidance for conducting systematic scoping reviews. JBI Evid. Implement. 13, 141–146 (2015).
Muka, T. et al. A 24-step guide on how to design, conduct, and successfully publish a systematic review and meta-analysis in medical research. Eur. J. Epidemiol. 35, 49–60 (2020).
Tricco, A. C. et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann. Intern. Med. 169, 467–473 (2018).
Haddaway, N. R., Collins, A. M., Coughlin, D. & Kirk, S. The role of google scholar in evidence reviews and its applicability to grey literature searching. PLoS ONE 10, e0138237 (2015).
Raina, R., Madhavan, A. & Ng, A. Y. Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th Annual International Conference on Machine Learning 873–880 (Association for Computing Machinery, 2009).
World Bank Country and Lending Groups – World Bank Data Help Desk. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.
Harrison, H., Griffin, S. J., Kuhn, I. & Usher-Smith, J. A. Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation. BMC Med. Res. Methodol. 20, 7 (2020).
Methley, A. M., Campbell, S., Chew-Graham, C., McNally, R. & Cheraghi-Sohi, S. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv. Res. 14, 579 (2014).
Artificial Intelligence in Global Health: Defining a Collective Path Forward. https://www.usaid.gov/sites/default/files/documents/1864/AI-in-Global-Health_webFinal_508.pdf (2019).
Bereskin, Caulder, P. L.-I., Kovarik, R. & Cowan, C. AI in focus: BlueDot and the response to COVID-19. Lexology https://www.lexology.com/library/detail.aspx?g=a94f63b4-2829-4f62-97f7-43f2aecd12a6 (2020).
ASEAN BioDiaspora Virtual Center. COVID-19 Situational Report in the ASEAN Region. 16 https://asean.org/wp-content/uploads/2021/10/COVID-19_Situational-Report_ASEAN-BioDiaspora-Regional-Virtual-Center_11Oct2021-1.pdf (2021).
Smart delivery robot-Pudu robotics. Smart delivery robot-Pudu robotics https://www.pudutech.com/.
Simonite, T. Chinese Hospitals Deploy AI to Help Diagnose Covid-19. Wired.
Li, K. et al. Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system. Quant. Imaging Med. Surg. 11, 3629–3642 (2021).
Weinstein, E. China’s Use of AI in its COVID-19 Response. https://cset.georgetown.edu/publication/chinas-use-of-ai-in-its-covid-19-response/ (2020).
Liu, X. et al. A 2-year investigation of the impact of the computed tomography–derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management. Eur. Radiol. 31, 7039–7046 (2021).
Ruijin Hospital: Develop AI-powered chronic disease management products with 4Paradigm. 4Paradigm. https://en.4paradigm.com/content/details_262_1198.html.
Han, M. Langfang’s epidemic prevention and control strategy, robots are online on duty. Beijing Daily https://ie.bjd.com.cn/5b165687a010550e5ddc0e6a/contentApp/5b1a1310e4b03aa54d764015/AP5e4aae66e4b0c4aab142c4d8?isshare=1&app=8ED108F8-A848-43A8-B32F-83FD7330B638&from=timeline (2020).
Research on the Application of Intelligent Triage Innovation Technology in Southwest Medical University Hospital. Futong. http://www.futong.com.cn/intell-medical-case2.html (2020).
iFLYTEK Corporate Social Responsibility Report. https://www.iflytek.com/en/usr/uploads/2020/09/csr.pdf (2020).
Across China: Drones for blood deliveries take off in China – Xinhua | English.news.cn. http://www.xinhuanet.com/english/2021-03/27/c_139839745.htm (2021).
Truog, S., Lawrence, E., Defawe, O., Ramirez Rufino, S. & Perez Richiez, O. Medical Cargo Drones in Rural Dominican Republic. https://publications.iadb.org/publications/english/document/Medical-Cargo-Drones-in-Rural-Dominican-Republic.pdf (2020).
Knoblauch, A. M. et al. Bi-directional drones to strengthen healthcare provision: experiences and lessons from Madagascar, Malawi and Senegal. BMJ Glob. Health 4, e001541 (2019).
He, J. et al. Artificial intelligence-based screening for diabetic retinopathy at community hospital. Eye 34, 572–576 (2020).
Rajalakshmi, R., Subashini, R., Anjana, R. M. & Mohan, V. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye 32, 1138–1144 (2018).
Mollura, D. J. et al. Artificial intelligence in low- and middle-income countries: innovating global health radiology. Radiology 297, 513–520 (2020).
Partnerships. Alexapath. http://alexapath.com/Company/Partnership (2020).
Nakasi, R., Tusubira, J. F., Zawedde, A., Mansourian, A. & Mwebaze, E. A web-based intelligence platform for diagnosis of malaria in thick blood smear images: a case for a developing country. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 4238–4244 (IEEE, 2020).
Morales, H. M. P., Guedes, M., Silva, J. S. & Massuda, A. COVID-19 in Brazil—preliminary analysis of response supported by artificial intelligence in municipalities. Front. Digit. Health 3, 52 (2021).
Chinas AI doctor-bot help each doctor treat 600-700 patients daily. China Experience. https://www.china-experience.com/china-experience-insights/chinas-ai-doctor-bot-help-each-doctor-treat-600-700-patients-daily (2020).
Sapio Analytics launches ‘empathetic’ healthcare chatbot. MobiHealthNews https://www.mobihealthnews.com/news/asia/sapio-analytics-launches-empathetic-healthcare-chatbot (2021).
Index Labs TZ Company Limited. eShangazi is one-year-old! Medium https://medium.com/@indexlabstz/eshangazi-is-one-year-old-46b2b93978a4 (2018).
Patient Retention Solution. BroadReach Healthcare https://broadreachcorporation.com/patient-retention-solution/ (2020).
Sophisticated nudging in HIV: combining predictive analytics and behavioural insights. Indlela https://indlela.org/sophisticated-nudging-in-hiv-combining-predictive-analytics-and-behavioural-insights/ (2021).
Digital health: 5 innovative projects. Terre des hommes. https://www.tdh.ch/en/news/digital-health-5-innovative-projects (2021).
Ubenwa – 2019 In Review. https://www.ubenwa.ai/ubenwa-2019-highlight.html (2020).