Operation criteria for Artificial Intelligence in the use of traditional medicine for self-care in health
DOI:
https://doi.org/10.52428/20758944.v20i56.1209Keywords:
Artificial Intelligence, Digital Health, Traditional Medicine, Medicinal Plants, Self-Management, Global HealthAbstract
The guidelines defined in the Digital Health Strategy 2020 - 2025; the WHO Strategy on Traditional Medicine (2014 - 2025) and the Global Initiative on AI for Health allow understanding the current scenario and the road ahead towards universal health coverage. In this context, this work focuses on the management of information in Artificial Intelligence on the uses and application of medicinal plants of the Andean Amazonian Traditional Medicine for self-care in health. In the framework of the prevention of future or possible pandemics, this works applies a reverse engineering model to characterizing some elements and guidelines that allow the abstraction of notions of quality, safety, rigor and adequate and effective use of medicinal plants used in traditional Andean Amazonian and analyze its potential applications on AI technologies development from indigenous knowledge and the understanding of the natural patterns that guide the balance of the ecosystem.
Downloads
References
Adibaru Kiflie, M., Prasad Sharma, D., & Abebe Haile, M. (2024). Deep learning for Ethiopian indigenous medicinal plant species. Journal of Ayurveda and Integrative Medicine, 100987. Obtenido de https://pubmed.ncbi.nlm.nih.gov/39546923/
https://doi.org/10.1016/j.jaim.2024.100987
PMid:39546923 PMCid:PMC11613182
Alexander, S., Shareck, M., & Glenn, N. M. (2023). Capturing the Lived Experience of Place in Health Promotion Research: In Situ Methodologies. En D. P. Jourdan, Global Handbook of Health Promotion Research (págs. 115-128). Springer, Cham.
https://doi.org/10.1007/978-3-031-20401-2_11
Amabie, T., Chibueze Izah, S., & Chidozie Ogwu, M. &. (2024). Harmonizing Tradition and Technology: The Synergy of Artificial Intelligence in Traditional Medicine. En S. e. Izah, Herbal Medicine Phytochemistry. Reference Series in Phytochemistry (págs. 1-23). Springer, Cham. https://doi.org/10.1007/978-3-031-21973-3_70-1
Andres, F., Kawtrakul, A., Pechsiri, C., & Rajbhandari, S. (2009). Problems-Solving Map Extraction with Collective ntelligence Analysis and Language Engineering. En V. Prince, & M. Roche, Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration (págs. 325-343). Non identified: IGI Global. Obtenido de https://www.igi-global.com/chapter/problems-solving-map-extraction-collective/23069
https://doi.org/10.4018/978-1-60566-274-9.ch018
Asif, N. (2003). Reverse Engineering Methodology to Recover the Design Artifacts: A Case Study. Software Engineering Research and Practice, 932-938.
Belli, L., & Gaspar, W. (2023). The quest for AI sovereignty, transparency and accountability. IGF - Getulio Vargas Foundation. Obtenido de https://www.intgovforum.org/en/filedepot_download/288/26421
Biggerstaff, T. J. (1989). Design recovery for maintenance and reuse. Computer, 22(7), 36-49.
https://doi.org/10.1109/2.30731
Booth, D., & Jansen, B. J. (2009). A Review of Methodologies for Analyzing Websites. En B. J. Jansen, A. Spink, & I. Taksa, Handbook of Research on Web Log Analysis (págs. 143-164). Non identified: IGI Global. https://doi.org/10.4018/978-1-59904-974-8.ch008
Canavera, L. (2023). Blending indigenous knowledge and artificial intelligence to enable adaptation. The circle(1), 13. Obtenido de https://www.arcticwwf.org/magazine-issues/2023/innovating-for-a-better-future/
Chikofsky, E., & Cross, J. (1990). Reverse engineering and design recovery: a taxonomy. IEEE Software, 7(1), 13-17.
https://doi.org/10.1109/52.43044
Cossio, I. (2024). Detección automática de imágenes de plantas medicinales usando redes neuronales artificiales. Proyecto de Grado, Universidad Mayor de San Simón. Obtenido de https://www.cs.umss.edu.bo/rep_tesis.jsp?codigo=3370&tipo_tes=1
Dragan, I. M., & Isaic-Maniu, A. (2013). Snowball Sampling Completion. Journal of Studies in Social Sciences, 160-177. Obtenido de https://core.ac.uk/download/pdf/229607586.pdf
Estrada Lugo, E. (2002). Plantas Medicinales de México. Texcoco, México: Universidad Autónoma de Chapingo.
Etikan, I. e. (2015). Comparison of Convenience Sampling and Purposive Sampling. American Journal of Theoretical and Applied Statistics, 1. Obtenido de https://www.sciencepublishinggroup.com/article/10.11648/j.ajtas.20160501.11
https://doi.org/10.11648/j.ajtas.20160501.11
Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2). https://doi.org/10.2196/jmir.3.2.e20
PMid:11720962 PMCid:PMC1761894
Foley, R., & et al. (2020). Disciplined research in undisciplined settings": Critical explorations of in situ and mobile methodologies in geographies of health and wellbeing. AREA, 514-522. https://doi.org/10.1111/area.12604
Forslund, M., Mathieson, K., Djibo, Y., Mbindyo, C., Lugangira, N., & Balasubramaniam, P. (2024). Strengthening the evidence base on the use of digital health technologies to accelerate progress towards universal health coverage. Oxford Open Digital Health, 2. Obtenido de
https://doi.org/10.1093/oodh/oqae033
G., a., S., G., S.M.A., A., F., T., & M.H., G. R. (2022). Prevalence of self-medication during COVID-19 pandemic: A systematic review and meta-analysis. Front Public Health, 10. https://doi.org/10.3389/fpubh.2022.1041695
PMid:36408026 PMCid:PMC9669079
Ghimire, P. (2021). Digitalization of Indigenous Knowledge in Nepal. Review article. Acta Informatica Malaysia, 42-47. Obtenido de https://actainformaticamalaysia.com/archives/AIM/2aim2021/2aim2021-42-47.pdf
https://doi.org/10.26480/aim.01.2021.42.47
Girault, L. (1987). Kallawaya: curanderos itinerantes de Los Andes . La Paz, Bolivia: Quipus.
González Zepeda, L. E., & Martínez Pinto, C. E. (2023). Inteligencia Artificial centrada en los Pueblos Indígenas. Perspectivas desde América Latina y el Caribe. Montevideo, Uruguay: UNESCO.
Green Stócel, A. (2011). Significados de Vida: Espejo de nuestra Memoria en Defensa de la Madre Tierra. Medellín: Universidad de Antioquia. Obtenido de https://bibliotecadigital.udea.edu.co/bitstream/10495/6935/1/AbadioGreen_2011_MadreTierra.pdf
Gupta, C., & Sharma, A. (2024). Reviving Indigenous Languages using Machine Learning. Insights2Techinfo, 1. Obtenido de https://insights2techinfo.com/reviving-indigenous-languages-using-machine-learning/
Hubbard, T. L. (2002). Some Correspondences and Similarities of Shamanism and Cognitive Science: Interconnectedness, Extension of Meaning and Attribution of Mental States. Anthropology of Consciousness, 26-45. https://doi.org/10.1525/ac.2002.13.2.26
Hubbard, T. L. (2003). Further Correspondences and Similarities of Shamanism and Cognitive Science: Mental Representation, Implicit Processing, and Cognitive Structures. Anthropology of Consciousness, 40-74.
https://doi.org/10.1525/ac.2003.14.1.40
IKSLabs. (20224). Protocols for Non-Indigenous People Working with Indigenous Knowledge. Algoma: Deakin University. Obtenido de https://justiceactionmaribyrnong.com/wp-content/uploads/2024/09/A_protocol_bundle_for_working_with_Indigenous_knowledge_1725078558.pdf
Jaarsma, T., Strömberg, A., Dunbar, S., Fitzsimons, D., Lee, C., Middleton, S., . . . B., R. (2020). Self-care research: How to grow the evidence base? International Journal of Nursing Studies, 103555.
https://doi.org/10.1016/j.ijnurstu.2020.103555
PMid:32199150
Katz, J., & Csordas, T. J. (2003). Phenomenological ethnography in sociology and anthropology. Sage Journals, 275-288.
https://doi.org/10.1177/146613810343001
Khan, S. e. (2022). Did the COVID-19 pandemic impact help-seeking behavior for seizure management? A Google Trends™ study. Epilepsy and Behavior, 108489. https://doi.org/10.1016/j.yebeh.2021.108489
PMid:34920346 PMCid:PMC8669502
Lee, D. (2024). Silicon Snowball Sampling: A Dynamic Approach to Online Data Collection. 7th International Conference on New Trends in Social Sciences (págs. 1-10). Prague, Czech Republic: Diamond Scientific Publishing. Obtenido de https://www.researchgate.net/publication/383649541_Silicon_Snowball_Sampling_A_Dynamic_Approach_to_Online_Data_Collection
Lewis, J. (2020). Indigenous Protocol and Artificial Intelligence Position Paper. The Initiative for Indigenous Futures and the Canadian Institute for Advanced Research (CIFAR). doi:10.11573
Lewis, J. E. (2024). Abundant intelligences: placing AI within Indigenous knowledge frameworks. Ai & Society, 0.
https://doi.org/10.1007/s00146-024-02099-4
Llamazares, A. M. (2013). Occidente Herido: El Potencial Sanador del Chamanismo en el Mundo Contemporáneo. Diversidad, 67-104. Obtenido de https://ri.conicet.gov.ar/bitstream/handle/11336/28753/CONICET_Digital_Nro.6063bdfe-9243-4517-8147-5c45a3eece42_A.pdf?sequence=2&isAllowed=y
Lu, L. e. (2024). AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine. JMIR Medical Informatics, e58491.
PMid:38941141 PMCid:PMC11245652
Luccioni, S., Trevelin, B., & Mitchell, M. (3 de Septiembre de 2024). Hugging Face. Obtenido de https://huggingface.co/blog/sasha/ai-environment-primer
Luna, L. E. (1986). Vegetalismo: Shamanism Among the Mestizo Population of the Peruvian Amazon. Almqvist & Wiksell International.
OMS. (2013). Estrategia de la OMS sobre medicina tradiciona 2014-2023. Obtenido de https://iris.who.int/bitstream/handle/10665/95008/9789243506098_spa.pdf
OPS. (2022). Directrices de la OMS sobre intervenciones de autocuidado para la. Washington. Obtenido de https://doi.org/10.37774/9789275326275
Ortíz, A. e. (2003). Estudio de poblaciones ocultas y de difícil acceso. En Varios, Metodología para la elaboración de estudios epidemiológicos a nivel nacional y local y estudios para grupos especiales relacionados con las adicciones. (págs. 101-112). México: Observatorio Epidemiológico en México. Obtenido de https://www.researchgate.net/profile/Jorge-Villatoro-Velazquez/publication/277587690_Estudio_de_poblaciones_ocultas_y_de_dificil_acceso/links/556dd0b008aeccd7773f3c23/Estudio-de-poblaciones-ocultas-y-de-dificil-acceso.pdf
Oxford Insights. (2023). AI for Climate Change: Using artificial and indigenous Intelligence to fight climate change. Obtenido de https://oxfordinsights.com/insights/ai-indigenous-intelligence/
Pasikowski, S. (2023). Snowball Sampling and Its Non-Trivial Nature. Przegląd Badań Edukacyjnych Educational Studies Review, 105-120.
https://doi.org/10.12775/PBE.2023.030
Peredo Albornoz, G., & Prado Mendoza, C. (2024). Sistema Tukuypaq. Aportes de la medicina tradicional para el abordaje integral de epidemias o pandemias. Revista de Investigación e Información en Salud, 94-108. https://doi.org/10.52428/20756208.v19i47.1218
Peredo, G. (2020). Estudio de la fragmentación de líneas temporales y los estados prolongados de confusión para el análisis de la existencia-consciencia humana del siglo XXI. En Comuniteca, Diálogos de pandemia: Aportes y desafíos de la comunicación en tiempos de Covid-19 (págs. 107-111).
Pérez, R. (2019). Aplicación móvil para la preservación de las lenguas originarias de México aplicando Inteligencia Artificial. CIERMMI Mujeres en la ciencia(1), 75-96. https://doi.org/10.35429/H.2019.1.75.96
Poulose, N. (2021). Fake News in Health and Medicine. En Data Science for Fake News. The Information Retrieval Series. Springer.
https://doi.org/10.1007/978-3-030-62696-9
PMid:34384074
Prado Mendoza, C. (2016). Mancharisqa no es depresión. Aporte etnomédico para la salud mental intercultural. Cochabamba: No identificada. Obtenido de https://searchworks.stanford.edu/view/11879402
Prado, C. (2023). Memoria de Medicina Tradicional Andino Amazónica. 1000 Maneras de Prevenir y Curar Enfermedade. Centro Cultural Kuska de Sabidurías Ancestrales. Qinti Studios.
Redvers, N. e. (2024). Indigenous Elders' voices on health-systems change informed by planetary health: a qualitative and relational systems mapping inquiry. The Lancet Planetary Health, e1106-17. Obtenido de https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(24)00277-8/fulltext
https://doi.org/10.1016/S2542-5196(24)00277-8
PMid:39674198
Rojas, J. (2014). Vigilancia de la comunicaiones en la era digital - reporte Bolivia. Global Informatio Society Watch, Fundacion REDES para el Desarrollo Sostenible.
Rösing, I. (1995). Dialogos con divinidades de cerros, rayos, manantiales y lagos: oraciones blancas Kallawayas. Hisbol.
Ruíz Méndez, M. d., & Aguirre Aguilar, G. (2015). Etnografía virtual, un acercamiento al método y a sus aplicaciones. Estudios sobre las Culturas Contemporáneas, 67-96. Obtenido de https://dialnet.unirioja.es/servlet/articulo?codigo=5175390
Sakur, F. e. (2022). Self-care Behaviors and Technology Used During COVID-19: Systematic Review. JMIR Human Factors, e35173.
PMid:35442904 PMCid:PMC9217152
Sassi, S., & al., e. (2022). Collective intelligence and knowledge exploration: an introduction. International Journal of Data Science and Analytics, 99-111. https://doi.org/10.1007/s41060-022-00338-9
PMid:35730041 PMCid:PMC9205147
Sørensen, V., & Lansing, J. S. (2023). Art, technology and the Internet of Living Things. AI & Society, 2401-2417.
https://doi.org/10.1007/s00146-023-01667-4
PMid:37358942 PMCid:PMC10187521
Subbiah, V. (2023). The next generation of evidence-based medicine. Nature Medicine, 49-58. https://doi.org/10.1038/s41591-022-02160-z
PMid:36646803
Torres-Soto, N. Y. (2022). The relationship between self-care, positive family environment, and human wellbeing. Wellbeing, Space and Society, 100076. doi:https://doi.org/10.1016/j.wss.2022.100076
UIT. (2023). Obtenido de AI for Good: https://aiforgood.itu.int/about-ai-for-good/
UIT. (2023). Focus Group on "Artificial Intelligence for Health". Obtenido de https://www.itu.int/en/ITU-T/focusgroups/ai4h/Pages/default.aspx
Vanderbroek, I. (2003). Plantas medicinales para la atención primaria de la salud. El conocimiento de ocho médicos tradicionales de Apillapampa (Bolivia). Cochabamba: Industrias Gráficas Serrano.
Venkatesmarlu, V., & al., e. (2024). The Future of Healthcare: Using AI and IoT to Drive Data Driven Revolution. En S. Kant Gupta, D. A. Karras, & R. (. Natarajan, Revolutionizing Healthcare: AI Integration with IoT for Enhanced Patient Outcomes (págs. 169-183). Non identified: Springer, Cham. https://doi.org/10.1007/978-3-031-65022-2_9
Walker, M. (2023). Digital Health: How modern technology is changing medicine and.
White, J. B. (2024). Consilience and AI as technological prostheses. AI & Society, 2179-2181. Obtenido de https://philarchive.org/rec/WHICAA-6
https://doi.org/10.1007/s00146-024-02048-1
WHO. (2021). Global strategy on digital health 2020-2025. Obtenido de https://www.who.int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf
WHO. (2024). Implementation of self-care interventions for health and well-being. Guidance for health systems.
Wilson, E. O. (1999). Consilience. The Unity of KNowledge. Vintage Books.
Wu, C. e. (2022). Artificial Intelligence in Traditional Medicine. Frontiers in Pharmacology, 1-2. https://doi.org/10.3389/fphar.2022.933133
PMid:35991902 PMCid:PMC9386475
Yunkaporta, T. (2019). Sand Talk. How Indigenous Thinking can Save the World. Melbourn, Australia: Text Publishing
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Natalia Caballero Medina
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
