Evidence map›Paper›PMID 40478396›Full record

ArticleJournal of cancer survivorship : research and practice2025

Feasibility and preliminary efficacy of an artificial intelligence-based cancer survivorship care plan service using digital platforms: a single-arm prospective pilot study.

Soo Hyun Kim, Shin-Young Park, Sung Mook Lim, Hyemin Yi

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Article in Journal of cancer survivorship : research and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Soo Hyun KimSchool of Nursing, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, South Korea. soohyun@inha.ac.kr.
Shin-Young ParkDepartment of Surgery, Inha University Hospital & College of Medicine, Incheon, South Korea.
Sung Mook LimDepartment of Surgery, Inha University Hospital & College of Medicine, Incheon, South Korea.
Hyemin YiSchool of Nursing, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, South Korea.

Funding

National Research Foundation of Korea grant funded by the Korea government (Ministry of Science and ICT) RS-2022-NR070003
6 · The paper itself

Abstract

purposeThis study tested the feasibility and preliminary efficacy of an artificial intelligence (AI)-based survivorship care plan (SCP) service (named "quality of life [QOL] + intervention") for posttreatment breast cancer survivors using digital platforms.

methodsA single-arm prospective pilot study was conducted, and 38 posttreatment breast cancer survivors in South Korea were enrolled. The QOL + intervention consisted of 1) collection of patient-reported outcomes data from survivors via a mobile app, 2) automated generation of SCP via a website, and 3) in-person SCP review and counseling. Feasibility was assessed using study participation rate, retention rate, mobile app access rate, task completion rate, intervention satisfaction, etc. Preliminary efficacy was evaluated using changes in patient-provider interaction, patient activation, cancer survivors' self-efficacy, and QOL from baseline to 1 week after the intervention.

resultsFeasibility assessment showed promising results, with a 70.7% participation rate, an 89.5% retention rate, an 88.2% mobile app access rate, a 91.2% task completion rate, and an 88.2% satisfaction rate. The QOL + intervention improved patient activation (p = 0.051, effect size = 0.29), self-efficacy (p = 0.040, effect size = 0.31), and QOL (p = 0.010, effect size = 0.42). However, no significant change occurred in patient-provider interaction after the intervention (p = 0.098).

conclusionsThe QOL + intervention was feasible and preliminarily effective in improving patient outcomes among posttreatment breast cancer survivors in South Korea. To obtain robust evidence, large-scale randomized controlled trials are warranted in future studies. IMPLICATIONS FOR CANCER SURVIVORS: Survivorship care can be enhanced via AI-based digital technology. Trial registration KCT0009302.

Indexed as

Artificial intelligenceBreast cancer survivorsMobile appSurvivorship care plan

Identifiers

PMID40478396

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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.