Evidence map›Paper›PMID 31293245›Full record

ArticleJournal of medical Internet research2019

Artificial Intelligence and the Implementation Challenge.

James Shaw, Frank Rudzicz, Trevor Jamieson, Avi Goldfarb

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
135citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

135 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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75 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

James ShawWomen's College Hospital, Institute for Health System Solutions and Virtual Care, Toronto, ON, Canada.ORCID 0000-0002-9522-0756
Frank RudziczInternational Centre for Surgical Safety, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada.ORCID 0000-0002-1139-3423
Trevor JamiesonWomen's College Hospital, Institute for Health System Solutions and Virtual Care, Toronto, ON, Canada.ORCID 0000-0002-1050-3918
Avi GoldfarbRotman School of Management, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-8919-5814

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundApplications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed.

objectiveIn this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS).

methodsAfter providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work.

resultsUltimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation.

conclusionsIf the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.

Indexed as

Artificial IntelligenceHumansMachine LearningTelemedicineartificial intelligenceethicsimplementation sciencemachine learning

Identifiers

PMID31293245
PMCPMC6652121

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.