Evidence map›Paper›PMID 39354272›Full record

ReviewDigestive diseases and sciences2024

A Comprehensive Examination of ChatGPT's Contribution to the Healthcare Sector and Hepatology.

Kabita Kumari, Sharvan Kumar Pahuja, Sanjeev Kumar

Abstract readReview
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In one paragraph

Review in Digestive diseases and sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

3 authors.

Kabita KumariDepartment of Instrumentation and Control Engineering, Dr B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India. kabitak.ic.19@nitj.ac.in.
Sharvan Kumar PahujaDepartment of Instrumentation and Control Engineering, Dr B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India.
Sanjeev KumarBiomedical Instrumentation Unit, CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence and Natural Language Processing technology have demonstrated significant promise across several domains within the medical and healthcare sectors. This technique has numerous uses in the field of healthcare. One of the primary challenges in implementing ChatGPT in healthcare is the requirement for precise and up-to-date data. In the case of the involvement of sensitive medical information, it is imperative to carefully address concerns regarding privacy and security when using GPT in the healthcare sector. This paper outlines ChatGPT and its relevance in the healthcare industry. It discusses the important aspects of ChatGPT's workflow and highlights the usual features of ChatGPT specifically designed for the healthcare domain. The present review uses the ChatGPT model within the research domain to investigate disorders associated with the hepatic system. This review demonstrates the possible use of ChatGPT in supporting researchers and clinicians in analyzing and interpreting liver-related data, thereby improving disease diagnosis, prognosis, and patient care.

Indexed as

GastroenterologyArtificial IntelligenceHealth Care SectorHumansLiver DiseasesNatural Language ProcessingApplicationArtificial intelligenceChatGPTHealth careHepatologyLiver disease

Identifiers

What OpenQuestion holds

Textmetadata
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.