Evidence map›Paper›PMID 37223340›Full record

ArticleCureus2023

ChatGPT-4 and the Global Burden of Disease Study: Advancing Personalized Healthcare Through Artificial Intelligence in Clinical and Translational Medicine.

Mohamad-Hani Temsah, Amr Jamal, Fadi Aljamaan, Jaffar A Al-Tawfiq, Ayman Al-Eyadhy

Abstract readEditorial
In one paragraph

Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed.

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  20. Assessing patients' needs in the follow-up after treatment for colorectal cancer-a mixed-method study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024
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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

5 authors.

Mohamad-Hani TemsahPediatric Intensive Care Unit, Department of Pediatrics, King Saud University Medical City, College of Medicine, King Saud University, Riyadh, SAU.
Amr JamalDepartment of Family and Community Medicine, College of Medicine, King Saud University, Riyadh, SAU.
Fadi AljamaanDepartment of Critical Care, College of Medicine, King Saud University, Riyadh, SAU.
Jaffar A Al-TawfiqDepartment of Specialty Internal Medicine and Quality, Johns Hopkins Aramco Healthcare, Dhahran, SAU.
Ayman Al-EyadhyPediatric Intensive Care Unit, Department of Pediatrics, College of Medicine, King Saud University, Riyadh, SAU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The fusion of insights from the comprehensive global burden of disease (GBD) study and the advanced artificial intelligence of open artificial intelligence (AI) chat generative pre-trained transformer version 4 (ChatGPT-4) brings the potential to transform personalized healthcare planning. By integrating the data-driven findings of the GBD study with the powerful conversational capabilities of ChatGPT-4, healthcare professionals can devise customized healthcare plans that are adapted to patients' lifestyles and preferences. We propose that this innovative partnership can lead to the creation of a novel AI-assisted personalized disease burden (AI-PDB) assessment and planning tool. For the successful implementation of this unconventional technology, it is crucial to ensure continuous and accurate updates, expert supervision, and address potential biases and limitations. Healthcare professionals and stakeholders should have a balanced and dynamic approach, emphasizing interdisciplinary collaborations, data accuracy, transparency, ethical compliance, and ongoing training. By investing in the unique strengths of both ChatGPT-4, especially its newly introduced features such as live internet browsing or plugins, and the GBD study, we may enhance personalized healthcare planning. This innovative approach has the potential to improve patient outcomes and optimize resource utilization, as well as pave the way for the worldwide implementation of precision medicine, thereby revolutionizing the existing healthcare landscape. However, to fully harness these benefits at both the global and individual levels, further research and development are warranted. This will ensure that we effectively tap into the potential of this synergy, bringing societies closer to a future where personalized healthcare is the norm rather than the exception.

Indexed as

ai-assisted personalized disease burdenchatbotschatgpt-4global burden of disease (gbd)personalized healthcare planprecision medicine

Identifiers

PMID37223340
PMCPMC10204616

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.