Evidence map›Paper›PMID 35577340›Full record

ArticleNeurospine2022

Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction.

Kyoung Hyup Nam, Da Young Kim, Dong Hwan Kim, Jung Hwan Lee, Jae Il Lee, Mi Jeong Kim, Joo Young Park, Jae Hyun Hwang, Sang Seok Yun, Byung Kwan Choi and 2 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurospine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 8% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025
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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

12 authors at 3 institutions in 1 country.

Kyoung Hyup NamDepartment of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Da Young KimHuman-Robot Interaction Center, Korea Institute of Robotics and Technology Convergence, Pohang, Korea.
Dong Hwan KimDepartment of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Jung Hwan LeeDepartment of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Jae Il LeeDepartment of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Mi Jeong KimDepartment of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Joo Young ParkDepartment of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Jae Hyun HwangDepartment of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Sang Seok YunDivision of Mechanical Convergence Engineering, Silla University, Busan, Korea.
Byung Kwan ChoiDepartment of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Min Gyu KimHuman-Robot Interaction Center, Korea Institute of Robotics and Technology Convergence, Pohang, Korea.
In Ho HanDepartment of Neurosurgery, Medical Research Institute, Pusan National University Hospital, Busan, Korea.
Pusan National University Hospital · KRKorea Institute of Robot and Convergence · KRSilla University · KR

Funding

Ministry of Trade, Industry and Energy 20000515
6 · The paper itself

Abstract

objectiveThe purpose of our study is to develop a spoken dialogue system (SDS) for pain questionnaire in patients with spinal disease. We evaluate user satisfaction and validated the performance accuracy of the SDS in medical staff and patients.

methodsThe SDS was developed to investigate pain and related psychological issues in patients with spinal diseases based on the pain questionnaire protocol. We recognized patients' various answers, summarized important information, and documented them. User satisfaction and performance accuracy were evaluated in 30 potential users of SDS, including doctors, nurses, and patients and statistically analyzed.

resultsThe overall satisfaction score of 30 patients was 5.5 ± 1.4 out of 7 points. Satisfaction scores were 5.3 ± 0.8 for doctors, 6.0 ± 0.6 for nurses, and 5.3 ± 0.5 for patients. In terms of performance accuracy, the number of repetitions of the same question was 13, 16, and 33 (13.5%, 16.8%, and 34.7%) for doctors, nurses, and patients, respectively. The number of errors in the summarized comment by the SDS was 5, 0, and 11 (5.2%, 0.0%, and 11.6 %), respectively. The number of summarization omissions was 7, 5, and 7 (7.3%, 5.3%, and 7.4%), respectively.

conclusionThis is the first study in which voice-based conversational artificial intelligence (AI) was developed for a spinal pain questionnaire and validated by medical staff and patients. The conversational AI showed favorable results in terms of user satisfaction and performance accuracy. Conversational AI can be useful for the diagnosis and remote monitoring of various patients as well as for pain questionnaires in the future.

Indexed as

ChatbotConversational artificial intelligenceNatural language processPain questionnaireSpineSpoken dialogue system

Identifiers

PMID35577340
PMCPMC9260557
OpenAlexW4280535060

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.