Copy and paste the code below, or use the file export link (if available for that format).

@article{
  author = {Miriam Stankovich},
  title = {AI and Big Data Deployment in Health Care: Proposing Robust and Sustainable Governance Solutions for Developing Country Governments},
  journal = {UNDP (United Nations Development Programme)},
  year = {2024},
  location = {New York},
  URL = {},
  abstract = {This paper is intended to identify both barriers to AI deployment at scale in developing countries and the types of regulatory and public policy actions that can best accelerate the appropriate use of AI to improve health care in developing countries. While AI technologies hold great potential for improving health care around the globe, they cannot be considered a panacea for solving global health challenges. Scaling up AI technologies has risks and trade-offs. The adoption, acceleration and use of AI should strengthen local health systems and be owned and driven by the needs and priorities of developing country governments and stakeholders to help them best serve their populations. This paper starts with a landscape assessment of AI and big data analytics deployment in developing countries. It considers three fields of AI deployment in diagnosis and clinical care, health research and drug development, and health systems management and planning. It outlines key challenges that need to be addressed by regulators in governing AI in health care, such as data access, data quality, data privacy and ethics. Lastly, the paper outlines key governance mechanisms for AI innovation in health care in developing countries, such as data collection and management, data sharing and open-source solutions for data de-identification, open-source data banks and data annotation.}
}

AU - Miriam Stankovich
TI - AI and Big Data Deployment in Health Care: Proposing Robust and Sustainable Governance Solutions for Developing Country Governments
PT - Journal Article
DP - 2024
TA - UNDP (United Nations Development Programme)
AB - This paper is intended to identify both barriers to AI deployment at scale in developing countries and the types of regulatory and public policy actions that can best accelerate the appropriate use of AI to improve health care in developing countries. While AI technologies hold great potential for improving health care around the globe, they cannot be considered a panacea for solving global health challenges. Scaling up AI technologies has risks and trade-offs. The adoption, acceleration and use of AI should strengthen local health systems and be owned and driven by the needs and priorities of developing country governments and stakeholders to help them best serve their populations. This paper starts with a landscape assessment of AI and big data analytics deployment in developing countries. It considers three fields of AI deployment in diagnosis and clinical care, health research and drug development, and health systems management and planning. It outlines key challenges that need to be addressed by regulators in governing AI in health care, such as data access, data quality, data privacy and ethics. Lastly, the paper outlines key governance mechanisms for AI innovation in health care in developing countries, such as data collection and management, data sharing and open-source solutions for data de-identification, open-source data banks and data annotation.

%0 Journal Article
%A Miriam Stankovich
%T AI and Big Data Deployment in Health Care: Proposing Robust and Sustainable Governance Solutions for Developing Country Governments
%D 2024
%J UNDP (United Nations Development Programme)
%U ,
%X This paper is intended to identify both barriers to AI deployment at scale in developing countries and the types of regulatory and public policy actions that can best accelerate the appropriate use of AI to improve health care in developing countries. While AI technologies hold great potential for improving health care around the globe, they cannot be considered a panacea for solving global health challenges. Scaling up AI technologies has risks and trade-offs. The adoption, acceleration and use of AI should strengthen local health systems and be owned and driven by the needs and priorities of developing country governments and stakeholders to help them best serve their populations. This paper starts with a landscape assessment of AI and big data analytics deployment in developing countries. It considers three fields of AI deployment in diagnosis and clinical care, health research and drug development, and health systems management and planning. It outlines key challenges that need to be addressed by regulators in governing AI in health care, such as data access, data quality, data privacy and ethics. Lastly, the paper outlines key governance mechanisms for AI innovation in health care in developing countries, such as data collection and management, data sharing and open-source solutions for data de-identification, open-source data banks and data annotation.

TY  - JOUR
AU  - Miriam Stankovich
TI  - AI and Big Data Deployment in Health Care: Proposing Robust and Sustainable Governance Solutions for Developing Country Governments
PY  - 2024
JF  - UNDP (United Nations Development Programme)
UR  - ,
AB  - This paper is intended to identify both barriers to AI deployment at scale in developing countries and the types of regulatory and public policy actions that can best accelerate the appropriate use of AI to improve health care in developing countries. While AI technologies hold great potential for improving health care around the globe, they cannot be considered a panacea for solving global health challenges. Scaling up AI technologies has risks and trade-offs. The adoption, acceleration and use of AI should strengthen local health systems and be owned and driven by the needs and priorities of developing country governments and stakeholders to help them best serve their populations. This paper starts with a landscape assessment of AI and big data analytics deployment in developing countries. It considers three fields of AI deployment in diagnosis and clinical care, health research and drug development, and health systems management and planning. It outlines key challenges that need to be addressed by regulators in governing AI in health care, such as data access, data quality, data privacy and ethics. Lastly, the paper outlines key governance mechanisms for AI innovation in health care in developing countries, such as data collection and management, data sharing and open-source solutions for data de-identification, open-source data banks and data annotation.

TY  - JOUR
T1  - AI and Big Data Deployment in Health Care: Proposing Robust and Sustainable Governance Solutions for Developing Country Governments
AU  - Miriam Stankovich
PY  - 2024
JF  - UNDP (United Nations Development Programme)
UR  - ,
AB  - This paper is intended to identify both barriers to AI deployment at scale in developing countries and the types of regulatory and public policy actions that can best accelerate the appropriate use of AI to improve health care in developing countries. While AI technologies hold great potential for improving health care around the globe, they cannot be considered a panacea for solving global health challenges. Scaling up AI technologies has risks and trade-offs. The adoption, acceleration and use of AI should strengthen local health systems and be owned and driven by the needs and priorities of developing country governments and stakeholders to help them best serve their populations. This paper starts with a landscape assessment of AI and big data analytics deployment in developing countries. It considers three fields of AI deployment in diagnosis and clinical care, health research and drug development, and health systems management and planning. It outlines key challenges that need to be addressed by regulators in governing AI in health care, such as data access, data quality, data privacy and ethics. Lastly, the paper outlines key governance mechanisms for AI innovation in health care in developing countries, such as data collection and management, data sharing and open-source solutions for data de-identification, open-source data banks and data annotation.