Evidence mapPaperPMID 41709519Full record

ReviewDiabetes/metabolism research and reviews2026

Algor-Ethics in Diabetes Care: Mapping the Route.

Joshua Bemporad, Francesco De Domenico, Paolo Pozzilli

Abstract readReview
In one paragraph

Review in Diabetes/metabolism research and reviews, 2026. 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.

Joshua BemporadCampus Bio-Medico University, Rome, Italy.ORCID 0009-0006-5535-3251
Francesco De DomenicoCampus Bio-Medico University, Rome, Italy.ORCID 0009-0007-2865-761X
Paolo PozzilliCampus Bio-Medico University, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes mellitus represents a multifaceted global health challenge, frequently coexisting with obesity, cardiovascular complications, and metabolic disorders. Effective management requires individualised, evidence-based decisions informed by an array of clinical, genetic, and lifestyle data. With the rapid growth of digital health technologies, artificial intelligence (AI) and algorithmic systems have emerged as powerful tools to support clinicians in diagnosis, treatment planning, and risk stratification. While AI shows promise in improving diabetes outcomes and health system efficiency, its integration into patient care is not without ethical and epistemic challenges. Algorithmic decision-making can influence therapeutic strategies, sometimes without full transparency or adequate oversight, potentially compromising human values such as autonomy, justice, and trust. In this context, the discipline of 'Algor-ethics', a term coined to describe the intersection of algorithmic systems and ethical principles, becomes critical. This article explores the foundational concepts of Algor-ethics applied to diabetes care, analyzes the current state of AI integration, and highlights the epistemic and ethical implications of algorithmic decision-making. Emphasis is placed on developing a framework that ensures AI is implemented safely, equitably, and responsibly, particularly for complex patients with diabetes.

Indexed as

AlgorithmsArtificial IntelligenceDiabetes MellitusDigital HealthHumansalgorethicsAlgor‐ethicsartificial intelligencediabetesdiabetes careethicsmachine learning

Identifiers

PMID41709519
PMCPMC12917293

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.