Evidence map›Paper›PMID 37379067›Full record

ArticleJournal of medical Internet research2023

Utility of ChatGPT in Clinical Practice.

Jialin Liu, Changyu Wang, Siru Liu

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 245 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
245citing papers in PubMed, 3 pooled it
18.5field-weighted citation impact, top 1% 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

245 citing papers in PubMed, 3 syntheses or guidelines pooled it, 484 citations in OpenAlex.

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  10. Artificial Intelligence Can Direct Patients Toward a Complaint-specific Musculoskeletal Provider.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
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185 more citing papers are in PubMed but not listed here.

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

3 authors at 2 institutions in 2 countries.

Jialin LiuInformation Center, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-1369-4625
Changyu WangInformation Center, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0003-4548-331X
Siru LiuDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID 0000-0002-5003-5354
Sichuan University · CNVanderbilt University Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ChatGPT is receiving increasing attention and has a variety of application scenarios in clinical practice. In clinical decision support, ChatGPT has been used to generate accurate differential diagnosis lists, support clinical decision-making, optimize clinical decision support, and provide insights for cancer screening decisions. In addition, ChatGPT has been used for intelligent question-answering to provide reliable information about diseases and medical queries. In terms of medical documentation, ChatGPT has proven effective in generating patient clinical letters, radiology reports, medical notes, and discharge summaries, improving efficiency and accuracy for health care providers. Future research directions include real-time monitoring and predictive analytics, precision medicine and personalized treatment, the role of ChatGPT in telemedicine and remote health care, and integration with existing health care systems. Overall, ChatGPT is a valuable tool that complements the expertise of health care providers and improves clinical decision-making and patient care. However, ChatGPT is a double-edged sword. We need to carefully consider and study the benefits and potential dangers of ChatGPT. In this viewpoint, we discuss recent advances in ChatGPT research in clinical practice and suggest possible risks and challenges of using ChatGPT in clinical practice. It will help guide and support future artificial intelligence research similar to ChatGPT in health.

Indexed as

Artificial IntelligenceOrganizationsClinical Decision-MakingDiagnosis, DifferentialDocumentationHumansartificial intelligencebarriersbest practiceschallengesChatGPTclinical practicecommunicationdoctor-patientguidanceguidelineslarge language modellarge language modelsnatural language processingNLPpatient-physicianrecommendationsrisks

Identifiers

PMID37379067
PMCPMC10365580
OpenAlexW4380995257

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.