AI for Social Good

While the AI research ecosystem is growing, there is currently still limited research into how AI can positively transform economies and societies. In light of this, United Nations ESCAP, APRU and Google partnered in 2018 to fill this void by developing a network of regional scholars to formulate policies and strategies that support, advance and maximize AI for Social Good.

The project draws on new insights for the development of a set of papers and report to inform senior policy makers, experts, and governments how to cultivate an ecosystem. The aim is to foster and enhance AI for social good within economies and identify what government approaches will address the challenges associated with AI while maximizing the technology’s potential.

Visit the UNESCAP-APRU-Google AI for Social Good Summit, November 2020 here.

Find out more about the two AI projects with the Thai Government here.

Find out more about the AI project with the Bangladeshi Government here.

Latest News
Responsible Data Sharing, Ai Innovation And Sandbox Development: Recommendations For Digital Health Governance
Author: Jasper Tromp, National University of Singapore Introduction: Thailand’s digital health landscape is evolving with efforts to embrace technology and leverage its potential benefits. The country has been making strides in digital health readiness, as reflected in its ranking of 59th globally in the Government AI Readiness Index 2021 and 9th in East Asia. However, several barriers and challenges exist in the digital health space. One of the primary challenges is the fragmented nature of healthcare service provision, affecting differences in data architecture, standards, and collection. Manual data management and the persistence of paper-based electronic health record systems also limit efficient data sharing and interoperability. Limited resources pose a significant barrier, with uneven human, technical, and financial resource distribution across healthcare institutions. High hardware and software acquisition, installation, and maintenance costs further impede engagement in quality data collection and sharing, particularly for smaller clinics and hospitals. Thailand faces a lack of understanding of the value of data and the importance of data security and privacy. Health literacy issues and confusion around data-sharing parameters also contribute to the challenges. Additionally, the absence of precise data-sharing regulations and guidelines at the political and policy levels creates uncertainty and hampers progress. While Thailand has made progress in its digital health landscape, addressing barriers related to data integration, standardization, resource allocation, and regulations is crucial to unlocking digital health initiatives’ full potential and achieving improved healthcare outcomes. Read this article for more information about the AI for Social Good project and the research in Thailand.
Artificial Intelligence in Pregnancy Monitoring: Technical Challenges for Bangladesh
Author: M Arifur Rahman, Hawai‘i Pacific University Introduction: This research paper examines the potential benefits of introducing an AI-enhanced pregnancy monitoring system in Bangladesh to enhance maternal health outcomes. Currently, the monitoring of pregnant women in Bangladesh lacks systematic approaches, and many women face limited access to such services due to various factors. Implementing personalized and continuous AI-enhanced monitoring for pregnant women has the potential to improve their health outcomes, but it necessitates access to significant amounts of data. While the Electronic Health Record/Electronic Medical Record (EHR/EMR) system is considered the ideal method for recording healthcare data, Bangladesh has yet to establish a universal EHR system. Although the country has plans to implement an EHR system by 2025, the process is time-consuming. This study assesses the existing maternity healthcare infrastructure in Bangladesh to identify opportunities for integrating AI and proposes recommendations for the government to introduce AI-enhanced pregnancy monitoring systems to address the identified challenges. The research methodology involves identifying technical challenges, gaps in the current technical infrastructure, and drawing lessons from previous project implementations for incorporating AI into pregnancy monitoring. However, as EHR serves as the foundational element for AI-enhanced healthcare, and establishing a universal EHR represents a significant IT undertaking, this study also examines major IT projects carried out by the Bangladesh government, identifies strengths and weaknesses, and provides recommendations for the next steps in EHR development, ultimately leading to AI-enhanced maternity health monitoring systems. Read this article for more information about the AI for Social Good project and the research in Bangladesh.

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