Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Impact and Challenges of Artificial Intelligence Integration in the African Health Sector: A Review
36
Zitationen
24
Autoren
2024
Jahr
Abstract
Artificial intelligence has proven to be a game-changing force in health sectors throughout Africa offering prospects for significant development.In sub-Saharan Africa, using AI in healthcare, especially in areas with limited resources, holds valuable promise in transforming and improving healthcare.This article takes an excellent look at how AI is being integrated into the African health sector, as well as examining policy frameworks, challenges and future possibilities.This article begins by giving an overview of AI and highlighting the groundbreaking impact of AI technologies in combating and addressing healthcare challenges that occur within African countries.Ranges from mobile-based diagnostics to precision medicine, artificial intelligence has proven its potential and capabilities in diagnosing, treating and improving healthcare operations by providing solutions to resource constraints and accessibility challenges.However, despite these advancements, there are still obstacles such as infrastructure limitations, concerns about data privacy and gaps in healthcare professionals' training that hinder the realization of AI's potential in African healthcare.This article envisions a future where the adoption of artificial intelligence is fully incorporated with community health initiatives and enhanced access to healthcare services for the betterment of healthcare across sub-Saharan African countries.While challenges and barriers like infrastructure and unequal access to healthcare persist, there is a need for governments and stakeholders to prioritize intelligence and digital health as catalysts for improving the healthcare sector in sub-Saharan Africa.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.239 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.095 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.463 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.428 Zit.
Autoren
- Elijah Kolawole Oladipo
- Stephen Feranmi Adeyemo
- Glory Jesudara Oluwasanya
- Omotayo Rachael Oyinloye
- Olawumi Hezekiah Oyeyiola
- Ifeoluwa David Akinrinmade
- Olubunmi Ayobami Elutade
- Dorcas Olayemi Areo
- Islamiyyah Olamide Hamzat
- Oluwakemi Deborah Olakanmi
- Israel Ifeoluwa Ayanronbi
- Akinwumi John Akanmu
- Faith Opeoluwa Ajekiigbe
- Mary Olawumi Taiwo
- Victor Michael Ogunfidodo
- Christiana Adewumi Adekunle
- Precious Oluwadamilola Adeleke
- David Ayo Olubunmi
- Precious Ayomide Adeogun
- Emmanuel Oluwagbenga Adejobi
- Sadiq Sanni
- Akinola Oluwatosin Ajibade
- Helen Onyeaka
- Nnabueze Darlington Nnaji