Evidence map›Paper›PMID 37578819›Full record

ArticleInteractive journal of medical research2023

Appropriateness and Comprehensiveness of Using ChatGPT for Perioperative Patient Education in Thoracic Surgery in Different Language Contexts: Survey Study.

Chen-Ye Shao, Hui Li, Xiao-Long Liu, Chang Li, Li-Qin Yang, Yue-Juan Zhang, Jing Luo, Jun Zhao

Open access · goldAbstract read
In one paragraph

Article in Interactive journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 11% 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

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.

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  14. Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 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

8 authors at 2 institutions in 1 country.

Chen-Ye Shao *Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.ORCID https://orcid.org/0009-0006-7288-816X
Hui Li *Department of Obstetrics and Gynecology, The First Affiliated Hospital of Soochow University, Suzhou, China.ORCID https://orcid.org/0000-0002-6190-6266
Xiao-Long Liu *Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.ORCID https://orcid.org/0000-0002-3969-1684
Chang LiDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.ORCID https://orcid.org/0000-0002-9469-7173
Li-Qin YangDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.ORCID https://orcid.org/0009-0007-2765-0977
Yue-Juan ZhangDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.ORCID https://orcid.org/0009-0006-9280-9393
Jing LuoDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.ORCID https://orcid.org/0000-0002-7400-7462
Jun ZhaoDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.ORCID https://orcid.org/0000-0001-8903-8205
Soochow University · CNNanjing General Hospital of Nanjing Military Command · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatGPT, a dialogue-based artificial intelligence language model, has shown promise in assisting clinical workflows and patient-clinician communication. However, there is a lack of feasibility assessments regarding its use for perioperative patient education in thoracic surgery.

objectiveThis study aimed to assess the appropriateness and comprehensiveness of using ChatGPT for perioperative patient education in thoracic surgery in both English and Chinese contexts.

methodsThis pilot study was conducted in February 2023. A total of 37 questions focused on perioperative patient education in thoracic surgery were created based on guidelines and clinical experience. Two sets of inquiries were made to ChatGPT for each question, one in English and the other in Chinese. The responses generated by ChatGPT were evaluated separately by experienced thoracic surgical clinicians for appropriateness and comprehensiveness based on a hypothetical draft response to a patient's question on the electronic information platform. For a response to be qualified, it required at least 80% of reviewers to deem it appropriate and 50% to deem it comprehensive. Statistical analyses were performed using the unpaired chi-square test or Fisher exact test, with a significance level set at P<.05.

resultsThe set of 37 commonly asked questions covered topics such as disease information, diagnostic procedures, perioperative complications, treatment measures, disease prevention, and perioperative care considerations. In both the English and Chinese contexts, 34 (92%) out of 37 responses were qualified in terms of both appropriateness and comprehensiveness. The remaining 3 (8%) responses were unqualified in these 2 contexts. The unqualified responses primarily involved the diagnosis of disease symptoms and surgical-related complications symptoms. The reasons for determining the responses as unqualified were similar in both contexts. There was no statistically significant difference (34/37, 92% vs 34/37, 92%; P=.99) in the qualification rate between the 2 language sets.

conclusionsThis pilot study demonstrates the potential feasibility of using ChatGPT for perioperative patient education in thoracic surgery in both English and Chinese contexts. ChatGPT is expected to enhance patient satisfaction, reduce anxiety, and improve compliance during the perioperative period. In the future, there will be remarkable potential application for using artificial intelligence, in conjunction with human review, for patient education and health consultation after patients have provided their informed consent.

Indexed as

AIartificial intelligenceChatGPTclinical workflowcommunicationeducationevaluationfeasibilityGenerative Pre-trained Transformerlanguagelanguage modelpatientpatient educationsurgerythoracicthoracic surgeryworkflow

Identifiers

PMID37578819
PMCPMC10463083
OpenAlexW4385299173

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.