Evidence map›Paper›PMID 39836955›Full record

Trial reportJournal of medical Internet research2025

Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial.

Ukrae Cho, Yong Nam Gwon, Seung Ryong Chong, Ji Yeon Han, Do Kyung Kim, Seung Whan Doo, Won Jae Yang, Kyeongmin Kim, Sung Ryul Shim, Jaehun Jung and 1 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
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

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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Ukrae Cho *AI product Biz Team, AI service division, SK Telecom, Seoul, Republic of Korea.ORCID 0009-0000-0242-303X
Yong Nam Gwon *Department of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, Seoul, Republic of Korea.ORCID 0000-0002-8732-327X
Seung Ryong ChongSocial Safety Net Team, ESG Office, SK Telecom, Seoul, Republic of Korea.ORCID 0009-0001-7148-0860
Ji Yeon HanDepartment of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, Seoul, Republic of Korea.ORCID 0009-0001-0984-0714
Do Kyung KimDepartment of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, Seoul, Republic of Korea.ORCID 0000-0002-3696-8756
Seung Whan DooDepartment of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, Seoul, Republic of Korea.ORCID 0000-0002-5924-6908
Won Jae YangDepartment of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, Seoul, Republic of Korea.ORCID 0000-0002-7356-4312
Kyeongmin KimDepartment of Engineering, University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-4814-9264
Sung Ryul ShimDepartment of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, Republic of Korea.ORCID 0000-0003-4143-7383
Jaehun Jung *Department of Preventive Medicine, Korea University College of Medicine, Seoul, Republic of Korea.ORCID 0000-0002-4856-3668
Jae Heon Kim *Department of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, Seoul, Republic of Korea.ORCID 0000-0002-4490-3610

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUreteral stents, such as double-J stents, have become indispensable in urologic procedures but are associated with complications like hematuria and pain. While the advancement of artificial intelligence (AI) technology has led to its increasing application in the health sector, AI has not been used to provide information on potential complications and to facilitate subsequent measures in the event of such complications.

objectiveThis study aimed to assess the effectiveness of an AI-based prediction tool in providing patients with information about potential complications from ureteroscopy and ureteric stent placement and indicating the need for early additional therapy.

methodsOverall, 28 patients (aged 20-70 years) who underwent ureteral stent insertion for the first time without a history of psychological illness were consecutively included. A "reassurance-call" service was set up to equip patients with details about the procedure and postprocedure care, to monitor for complications and their severity. Patients were randomly allocated into 2 groups, reassurance-call by AI (group 1) and reassurance-call by humans (group 2). The primary outcome was the level of satisfaction with the reassurance-call service itself, measured using a Likert scale. Secondary outcomes included satisfaction with the AI-assisted reassurance-call service, also measured using a Likert scale, and the level of satisfaction (Likert scale and Visual Analogue Scale [VAS]) and anxiety (State-Trait Anxiety Inventory and VAS) related to managing complications for both groups.

resultsOf the 28 recruited patients (14 in each group), 1 patient in group 2 dropped out. Baseline characteristics of patients showed no significant differences (all P>.05). Satisfaction with reassurance-call averaged 4.14 (SD 0.66; group 1) and 4.54 (SD 0.52; group 2), with no significant difference between AI and humans (P=.11). AI-assisted reassurance-call satisfaction averaged 3.43 (SD 0.94). Satisfaction about the management of complications using the Likert scale averaged 3.79 (SD 0.70) and 4.23 (SD 0.83), respectively, showing no significant difference (P=.14), but a significant difference was observed when using the VAS (P=.01), with 6.64 (SD 2.13) in group 1 and 8.69 (SD 1.80) in group 2. Anxiety about complications using the State-Trait Anxiety Inventory averaged 36.43 (SD 9.17) and 39.23 (SD 8.51; P=.33), while anxiety assessed with VAS averaged 4.86 (SD 2.28) and 3.46 (SD 3.38; P=.18), respectively, showing no significant differences. Multiple regression analysis was performed on all outcomes, and humans showed superior satisfaction than AI in the management of complications. Otherwise, most of the other variables showed no significant differences (P.>05).

conclusionsThis is the first study to use AI for patient reassurance regarding complications after ureteric stent placement. The study found that patients were similarly satisfied for reassurance calls conducted by AI or humans. Further research in larger populations is warranted to confirm these findings.

trial registrationClinical Research Information System KCT0008062; https://tinyurl.com/4s8725w2.

Indexed as

Artificial IntelligencePatient SatisfactionStentsUreterAdultAgedFemaleHumansMaleMiddle AgedUreteroscopyYoung AdultAIartificial intelligencecomplicationsinformation resourcespatientsrandomized controlled trialureteral stenturologic proceduresurology

Identifiers

PMID39836955
PMCPMC11795156

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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.