Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?

By B. Wahl, Aline Cossy-Gantner, S. Germann, N. Schwalbe. BMJ Global Health, 2018

617 citations recorded by the source corpus.

The field of artificial intelligence (AI) has evolved considerably in the last 60 years. While there are now many AI applications that have been deployed in high-income country contexts, use in resource-poor settings remains relatively nascent. With a few notable exceptions, there are limited examples of AI being used in such settings. However, there are signs that this is changing. Several high-profile meetings have been convened in recent years to discuss the development and deployment of AI applications to reduce poverty and deliver a broad range of critical public services. We provide a general overview of AI and how it can be used to improve health outcomes in resource-poor settings. We also describe some of the current ethical debates around patient safety and privacy. Despite current challenges, AI holds tremendous promise for transforming the provision of healthcare services in resource-poor settings. Many health system hurdles in such settings could be overcome with the use of AI and other complementary emerging technologies. Further research and investments in the development of AI tools tailored to resource-poor settings will accelerate realising of the full potential of AI for improving global health.

Subfields: Health Informatics, Public Health, Environmental and Occupational Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/edc3481236ccf9839906fcbdf7de9d67f5999d8e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Informatics

1. Glenn Cohen
2. Timo Minssen
3. Zeeshan Ahmed
4. Sara Gerke
5. Khalid Mohamed
6. Xinqi Dong
7. Saman Zeeshan

In Public Health, Environmental and Occupational Health

1. Glenn Cohen
2. Timo Minssen
3. Sara Gerke

In Radiology, Nuclear Medicine and Imaging

1. Zeeshan Ahmed
2. Khalid Mohamed
3. Xinqi Dong
4. David Mhlanga
5. Saman Zeeshan

Citations in the corpus

• Ethical and legal challenges of artificial intelligence-driven healthcare (2020, positive): “but to democratize expertise, “globalize” healthcare, and bring it to even remote areas [58].”
• Artificial Intelligence in the Industry 4.0, and Its Impact on Poverty, Innovation, Infrastructure Development, and the Sustainable Development Goals: Lessons from Emerging Economies? (2021, background): “[36] also stated that the field of AI has been developing in the last 60 years and various applications of AI have been utilized in high-income countries and their use in poor countries is still a huge challenge.”
• Bias in artificial intelligence algorithms and recommendations for mitigation (2023, methodology): “In addition, AI was proposed as a tool that could expand the reach of quality healthcare to underserved areas and improve health equity worldwide [12,13].”
• Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “Potential solutions to these concerns are based on design and implementation of healthcare IT infrastructure (35,49); EMR documentation and automation of repetitive tasks with ML algorithms (31); deep learning to mine (41), train (39) and learn complex relationships between features and labels (32);”