Evidence map›Paper›PMID 38594477›Full record

ReviewNPJ digital medicine2024

The potential for artificial intelligence to transform healthcare: perspectives from international health leaders.

Christina Silcox, Eyal Zimlichmann, Katie Huber, Neil Rowen, Robert Saunders, Mark McClellan, Charles N Kahn, Claudia A Salzberg, David W Bates

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 2 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

53 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  13. Assessing public interest in artificial intelligence in dermatology: A Google Trends analysis.JID innovations : skin science from molecules to population health · 2026
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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

9 authors.

Christina SilcoxDuke-Margolis Institute for Health Policy, Duke University, Washington, DC, USA &, Durham, NC, USA.
Eyal ZimlichmannSheba Medical Center, Ramat Gan, Israel.
Katie HuberDuke-Margolis Institute for Health Policy, Duke University, Washington, DC, USA &, Durham, NC, USA.ORCID http://orcid.org/0000-0003-2519-8714
Neil RowenDuke-Margolis Institute for Health Policy, Duke University, Washington, DC, USA &, Durham, NC, USA.
Robert SaundersDuke-Margolis Institute for Health Policy, Duke University, Washington, DC, USA &, Durham, NC, USA.
Mark McClellanDuke-Margolis Institute for Health Policy, Duke University, Washington, DC, USA &, Durham, NC, USA.
Charles N KahnFuture of Health, Washington, DC, USA.
Claudia A SalzbergFuture of Health, Washington, DC, USA.
David W BatesDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA. dbates@bwh.harvard.edu.ORCID http://orcid.org/0000-0001-6268-1540

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI's potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

Identifiers

PMID38594477
PMCPMC11004157

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.