Evidence map›Paper›PMID 37822851›Full record

ReviewJournal of clinical medicine research2023

Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation.

Debargha Basuli, Sasmit Roy

Abstract readReview
In one paragraph

Review in Journal of clinical medicine research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

2 authors.

Debargha BasuliDepartment of Nephrology and Hypertension, Brody School of Medicine/East Carolina University, Greenville, NC, USA.
Sasmit RoyDepartment of Internal Medicine, Centra Lynchburg General Hospital, Lynchburg, VA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of kidney transplantation is being revolutionized by the integration of artificial intelligence (AI) and machine learning (ML) techniques. AI equips machines with human-like cognitive abilities, while ML enables computers to learn from data. Challenges in transplantation, such as organ allocation and prediction of allograft function or rejection, can be addressed through AI-powered algorithms. These algorithms can optimize immunosuppression protocols and improve patient care. This comprehensive literature review provides an overview of all the recent studies on the utilization of AI and ML techniques in the optimization of immunosuppression in kidney transplantation. By developing personalized and data-driven immunosuppression protocols, clinicians can make informed decisions and enhance patient care. However, there are limitations, such as data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Future research should validate and refine AI models for different populations and treatment durations. AI and ML have the potential to revolutionize kidney transplantation by optimizing immunosuppression and improving outcomes. AI-powered algorithms enable personalized and data-driven immunosuppression protocols, enhancing patient care and decision-making. Limitations include data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Further research is needed to validate and enhance AI models for different populations and longer-term dosing decisions.

Indexed as

Artificial intelligenceImmunosuppressionKidney transplantMachine learning

Identifiers

PMID37822851
PMCPMC10563819

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.