• Sun. Dec 3rd, 2023

Healthcare Definition

Healthcare Definition, You Can't Live Withou It.

Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liang, H. et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat. Med. 25, 433–438 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Steinhubl, S. R., Muse, E. D. & Topol, E. J. The emerging field of mobile health. Sci. Transl. Med. 7, 283rv3 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Goldhahn, J., Rampton, V. & Spinas, G. A. Could artificial intelligence make doctors obsolete? BMJ 363, k4563 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 368, m689 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Coiera, E. The last mile: where artificial intelligence meets reality. J. Med. Internet Res. 21, e16323 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Asan, O. & Choudhury, A. Research trends in artificial intelligence applications in human factors health care: mapping review. JMIR Hum. Factors 8, e28236 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • Nundy, S., Montgomery, T. & Wachter, R. M. Promoting trust between patients and physicians in the era of artificial intelligence. JAMA 322, 497–498 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Gafni, R. & Pavel, T. Cyberattacks against the health-care sectors during the COVID-19 pandemic. Inf. Comput. Secur. 30, 137–150 (2021).

    Article 

    Google Scholar
     

  • Venkatesh, V. & Bala, H. Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39, 273–315 (2008).

    Article 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sanyal, C., Stolee, P., Juzwishin, D. & Husereau, D. Economic evaluations of eHealth technologies: a systematic review. PLoS ONE 13, e0198112 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chawla, S. et al. Electricity and generator availability in LMIC hospitals: improving access to safe surgery. J. Surg. Res. 223, 136–141 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ng, M. et al. Effective coverage: a metric for monitoring universal health coverage. PLoS Med. 11, e1001730 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Arksey, H. & O’Malley, L. Scoping studies: towards a methodological framework. Int. J. Soc. Res. Methodol. 8, 19–32 (2005).

    Article 

    Google Scholar
     

  • Peters, M. D. J. et al. Guidance for conducting systematic scoping reviews. JBI Evid. Implement. 13, 141–146 (2015).


    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • Tricco, A. C. et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann. Intern. Med. 169, 467–473 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • He, J. et al. Artificial intelligence-based screening for diabetic retinopathy at community hospital. Eye 34, 572–576 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mollura, D. J. et al. Artificial intelligence in low- and middle-income countries: innovating global health radiology. Radiology 297, 513–520 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • 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).

  • link

    By admin

    Leave a Reply

    Your email address will not be published. Required fields are marked *