Evidence map›Paper›PMID 42138868›Full record

ArticleRevista do Colegio Brasileiro de Cirurgioes2026

Application of artificial intelligence as postoperative support for patients undergoing thoracic surgery.

Beatriz D'Ávila Pereira DA Silva, Ernesto Evangelista Neto, João Aléssio Juliano Perfeito, André Miotto

Abstract read
In one paragraph

Article in Revista do Colegio Brasileiro de Cirurgioes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Beatriz D'Ávila Pereira DA Silva- Universidade Federal de São Paulo (UNIFESP), Escola Paulista de Medicina - São Paulo - SP - Brasil.ORCID http://orcid.org/0009-0003-3892-3978
Ernesto Evangelista Neto- Universidade Federal de São Paulo (UNIFESP), Escola Paulista de Medicina - São Paulo - SP - Brasil.ORCID http://orcid.org/0000-0002-4292-4995
João Aléssio Juliano Perfeito- Universidade Federal de São Paulo (UNIFESP), Escola Paulista de Medicina - São Paulo - SP - Brasil.ORCID http://orcid.org/0000-0001-5958-2541
André Miotto- Universidade Federal de São Paulo (UNIFESP), Escola Paulista de Medicina - São Paulo - SP - Brasil.ORCID http://orcid.org/0000-0002-4260-0595

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial intelligence (AI) is playing an increasingly significant role in medicine, from imaging-based diagnosis to personalized treatment. In the postoperative period, it may enhance patient support by answering questions more clearly and reducing the need for in-person visits. However, its reliability and effects on the patient experience remain insufficiently studied. This study examined the potential of AI as a complementary tool in the care of patients undergoing thoracic surgery, assessing response accuracy, patient acceptance, and its potential to reshape postoperative follow-up.

methodsReal patient questions were collected and answered by both ChatGPT and specialist physicians. The responses were compared in terms of clarity, accuracy, and completeness. Participants evaluated their satisfaction with the information provided, their need to seek additional details, and their comfort level with using AI without medical supervision.

resultsAmong the patients evaluated, 74.3% reported that ChatGPT fully addressed their questions, and 91.4% found the language clear and accessible. However, 62.8% still indicated a need for medical confirmation. In addition, 51.4% of participants stated that they would seek additional information even after receiving an AI-generated response.

conclusionThe findings suggest that AI has strong potential to enhance patients' postoperative experience by providing rapid, accessible answers. However, its use should be integrated into a hybrid care model that combines technology with individualized medical oversight.

Indexed as

Artificial IntelligencePostoperative CareThoracic Surgical ProceduresAdultFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedPatient Satisfaction

Identifiers

PMID42138868
PMCPMC13142958

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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.