Artificial intelligence, machine learning and health systems
Name
jogh-08-020303.pdf
Description
Published version
Size
1.23 MB
Format
Adobe PDF
Checksum (MD5)
c2085f3de75395f92b4402ed1612b7d1
Author(s)
Panch, Trishan
Szolovits, Peter
Atun, Rifan
Date Issued
December 2018
Journal
Journal of Global Health
Publisher
Edinburgh University Global Health Society
Citation
Panch, Trishan, et al. “Artificial Intelligence, Machine Learning and Health Systems.” Journal of Global Health 8, 2 (December 2018): 020303.
Version
Final published version
Abstract
Globally, health systems face multiple challenges: rising burden of illness, multimorbidity and disability driven by ageing and epidemiological transition, greater demand for health services, higher societal expectations and increasing health expenditures. A further challenge relates to inefficiency, with poor productivity. These health system challenges exist against a background of fiscal conservatism, with misplaced economic austerity policies that are constraining investment in health systems. Fundamental transformation of health systems is critical to overcome these challenges and to achieve universal health coverage (UHC) by 2030. Machine learning, the most tangible manifestation of artificial intelligence (AI) – and the newest growth area in digital technology – holds the promise of achieving more with less, and could be the catalyst for such a transformation. But the nature and extent of this promise has not been systematically assessed. To date, the impact of digital technology on health systems has been equivocal. Is AI the ingredient for such a transformation, or will it face the same fate as earlier attempts at introducing digital technology? In this paper, we explore potential applications of AI in health systems and the ways in which AI could transform health systems to achieve UHC by improving efficiency, effectiveness, equity and responsiveness of public health and health care services.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.7189/JOGH.08.020303
