Evidence map›Paper›PMID 32908264›Full record

ReviewNature2020

Illuminating the dark spaces of healthcare with ambient intelligence.

Albert Haque, Arnold Milstein, Li Fei-Fei

Registry-linked trialAbstract readReview
PubMed Publisher
In one paragraph

Review in Nature, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05939154 (Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine), which is not on this map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed
14.0field-weighted citation impact, top 1% of its field
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.

NCT05939154 unknown statusnot on this mapstarted 2023, after this paper: background citation

Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine

TypeobservationalSponsorShanghai Mental Health CenterRan2023 to 2025Enrolled400ConditionsMental Disorder
3 · Its place in the literature

Who cites it

64 citing papers in PubMed, 256 citations in OpenAlex.

  1. Article
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  8. A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026
    Review
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  13. Advances in 2D materials for wearable biomonitoring.Materials science & engineering. R, Reports : a review journal · 2025
    Article
  14. Article
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4 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

3 authors at 2 institutions in 1 country.

Albert HaqueDepartment of Computer Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-6769-6370
Arnold MilsteinClinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-2794-2378
Li Fei-FeiDepartment of Computer Science, Stanford University, Stanford, CA, USA. feifeili@stanford.edu.ORCID http://orcid.org/0000-0002-7481-0810
Stanford University · USStanford Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.

Indexed as

Ambient IntelligenceAlgorithmsChronic DiseaseDelivery of Health CareEnvironmental MonitoringHospital UnitsHumansMental HealthPatient SafetyPrivacy

Identifiers

PMID32908264
OpenAlexW3083918922

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.