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dc.contributor.authorPanch, Trishan
dc.contributor.authorSzolovits, Peter
dc.contributor.authorAtun, Rifan
dc.date.accessioned2020-03-27T18:00:30Z
dc.date.available2020-03-27T18:00:30Z
dc.date.issued2018-12
dc.identifier.issn2047-2986
dc.identifier.issn2047-2978
dc.identifier.urihttps://hdl.handle.net/1721.1/124393
dc.description.abstractGlobally, 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.en_US
dc.language.isoen
dc.publisherEdinburgh University Global Health Societyen_US
dc.relation.isversionofhttp://dx.doi.org/10.7189/JOGH.08.020303en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceJournal of Global Healthen_US
dc.titleArtificial intelligence, machine learning and health systemsen_US
dc.typeArticleen_US
dc.identifier.citationPanch, Trishan, et al. “Artificial Intelligence, Machine Learning and Health Systems.” Journal of Global Health 8, 2 (December 2018): 020303.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalJournal of Global Healthen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-07-10T17:39:30Z
dspace.date.submission2019-07-10T17:39:31Z
mit.journal.volume8en_US
mit.journal.issue2en_US
mit.metadata.statusComplete


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