Evidence map›Paper›PMID 36276884›Full record

ArticleAsia-Pacific journal of oncology nursing2022

A scoping review of deep learning in cancer nursing combined with augmented reality: The era of intelligent nursing is coming.

Yulan Ren, Yao Yang, Jiani Chen, Ying Zhou, Jiamei Li, Rui Xia, Yuan Yang, Qiao Wang, Xi Su

Abstract readScoping Review
In one paragraph

Article in Asia-Pacific journal of oncology nursing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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.

Yulan RenDepartment of Nursing, Guangzhou Medical University, Guangzhou, China.
Yao YangDepartment of Nursing, Guangzhou Medical University, Guangzhou, China.
Jiani ChenDepartment of Nursing, Guangzhou Medical University, Guangzhou, China.
Ying ZhouDepartment of Nursing, Guangzhou Medical University, Guangzhou, China.
Jiamei LiDepartment of Nursing, Guangzhou Medical University, Guangzhou, China.
Rui XiaDepartment of Nursing, Guangzhou Medical University, Guangzhou, China.
Yuan YangDepartment of Nursing, Guangzhou Medical University, Guangzhou, China.
Qiao WangDepartment of Gastrointestinal Surgery, TCM-Integrated Hospital of Southern Medical University, Guangzhou, China.
Xi SuDepartment of Nursing, Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence has been developing greatly in the field of medicine. As a new research hotspot of artificial intelligence, deep learning (DL) has been widely applied in the fields of cancer risk assessment, symptom recognition, and cancer detection. Therefore, nursing care issues in terms of consuming time and energy, lower accuracy, and lower efficiency can be solved with applying DL in caring cancer patients. In addition, augmented reality (AR) has great navigation potential through combining computer-generated virtual elements with the real world. Thus, DL ​+ ​AR may facilitate patients with cancer to possess a brand-new model of nursing care that is more intelligent, mobile, and adapted to the information age, compared to traditional nursing. With the advent of the era of intelligent nursing, future nursing models can not only learn from the DL ​+ ​AR model to meet the needs of patients with cancer but also reduce nursing workload, save healthcare resources, and improve work efficiency, the quality of nursing care, as well as the quality of life for cancer patients.

Indexed as

Artificial intelligenceAugmented realityCancer nursingDeep learningIntelligent nursing

Identifiers

PMID36276884
PMCPMC9579790

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.