Evidence mapPaperPMID 41049970Full record

ReviewCureus2025

Artificial Intelligence in Personalized Medicine for Diabetes Mellitus: A Narrative Review.

Kaushik Ghosh, Sudip Chandra, Sonali Ghosh, Uday S Ghosh

Abstract readReview
In one paragraph

Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Kaushik GhoshMedicine, Murshidabad Medical College and Hospital, Berhampore, IND.
Sudip ChandraBiostatistics, Social and Official Statistics Unit, Indian Statistical Institute, Kolkata, IND.
Sonali GhoshEmergency Medicine and Critical Care, Seth Sukhlal Karnani Memorial Hospital, Institute of Post Graduate Medical Education and Research, Kolkata, IND.
Uday S GhoshMedicine, Barasat Government Medical College and Hospital, Barasat, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes mellitus (DM) is a long-term metabolic condition involving persistent hyperglycemia, which causes morbidity, mortality, and economic stress. This article examines the role of artificial intelligence (AI)-initiated precision medicine in optimizing type 2 diabetes mellitus management in the Indian population. An exhaustive review of AI-based platforms used for diabetic treatment was performed, highlighting the combination of multidimensional data sets involving genetic, epigenetic, phenotypic, and environmental variables. The envisioned AI platform aims to offer personalized glycemic forecasts, tailored therapeutic interventions, complication monitoring, and stage-by-stage disease progression predictions. Precision medicine enabled by AI has shown promising outcomes in improving diabetes management through the administration of patient-specific treatment regimens, early glycemic change detection, and real-time monitoring of diabetes-related complications. The use of AI applications enables patients to follow evidence-based self-management behaviors, such as diet modifications, physical activity changes, insulin management, and continuous glucose monitoring. This patient-centered strategy enhances clinical efficacy, prevents long-term complications, and lowers healthcare costs. Additional longitudinal and multicentric trials are needed to confirm outcomes among heterogeneous cohorts and to fine-tune AI algorithms for increased clinical relevance and translational use.

Indexed as

ai toolsdiabetes mellitusglycemic fluctuationsindian phenotypepersonalized medicine (pm)

Identifiers

PMID41049970
PMCPMC12492464

What Socratic 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 Socratic graph.