Evidence map›Paper›PMID 36176267›Full record

ArticleAsia-Pacific journal of oncology nursing2022

Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review.

Lu-Chen Pan, Xiao-Ru Wu, Ying Lu, Han-Qing Zhang, Yao-Ling Zhou, Xue Liu, Sheng-Lin Liu, Qiao-Yuan Yan

Open access · goldAbstract 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 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 6% 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.

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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Applications of Artificial Intelligence (AI) in Breast Cancer Care Delivery and Education: A Scoping Review.International journal of environmental research and public health · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024
    Article
  17. Article
  18. Article
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

8 authors at 3 institutions in 1 country.

Lu-Chen PanDepartment of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Xiao-Ru WuSchool of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Ying LuDepartment of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Han-Qing ZhangHealth Science Center, Yangtze University, Jinzhou 434023, China.
Yao-Ling ZhouDepartment of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Xue LiuSchool of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Sheng-Lin LiuDepartment of Medical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Qiao-Yuan YanDepartment of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Union Hospital · CNHuazhong University of Science and Technology · CNYangtze University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process. Methods: Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review. Results: 43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors' care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing. Conclusions: While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.

Indexed as

Artificial intelligenceCancerDigital healthcare technologyInformaticsReviewSurvivorship care planning

Identifiers

PMID36176267
PMCPMC9513729
OpenAlexW4292964885

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.