Evidence map›Paper›PMID 35273459›Full record

ReviewCurrent genomics2021

Machine Learning in Healthcare.

Hafsa Habehh, Suril Gohel

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 176 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
176citing papers in PubMed, 10 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

176 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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  15. Observational
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  17. When Health Behavior Meets Technology: Artificial Neural Network-Based Prediction of Breast Self-Examination Intentions Among Filipino Female College Students.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026
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116 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

2 authors.

Hafsa HabehhDepartment of Health Informatics, Rutgers University School of Health Professions, 65 Bergen Street, Newark, NJ 07107, USA.
Suril GohelDepartment of Health Informatics, Rutgers University School of Health Professions, 65 Bergen Street, Newark, NJ 07107, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.

Indexed as

artificial intelligenceEHRgenomicshealthcareMachine learningsupport vector machine

Identifiers

PMID35273459
PMCPMC8822225

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.