INTEGRATING ARTIFICIAL INTELLIGENCE INTO HEALTHCARE AND HOSPITAL SYSTEMS

The Digital Health Learning Collaborative was established to support the
advancement of digital health and to recognize the growing significance of
data and analytics in achieving improved health outcomes. As the
collaboration has progressed, the rapid developments in artificial
intelligence (AI) and machine learning (ML)—with their remarkable
potential for both preventive and therapeutic care—have emerged as central
themes for the consortium.
This publication addresses a critical gap in the existing literature by offering
insights into the foundational concepts, current state-of-the-art applications,
and anticipated future impacts of AI and ML in healthcare. It is intended for
a wide audience, including clinicians (physicians, nurses, and allied health
professionals), data scientists, healthcare administrators, public health
officials, policymakers, regulators, healthcare purchasers, and patients.
Our aim is to provide relevant, understandable, and practical guidance on key
definitions, essential concepts, emerging trends, and potential risks in this
rapidly evolving field.
At the same time, it is important to recognize that many low-income
countries face significant challenges in keeping pace with global
technological advances. These nations often lack even basic healthcare
infrastructure and services, leading to significantly higher mortality rates.
According to the World Health Organization (WHO), the current 18.1- year
disparity in life expectancy between the world’s wealthiest and poorest
countries is largely due to inadequate or nonexistent access to healthcare.