Evidence map›Paper›PMID 41694900›Full record

ArticleCureus2026

Toward Ethical Governance of Artificial Intelligence (AI)-Enabled Cognitive Monitoring in Aging Populations.

Hana Abbasian

Abstract readEditorial
In one paragraph

Article in Cureus, 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

1 author.

Hana AbbasianCenter for Bioethics, Harvard Medical School, Boston, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AI-enabled cognitive monitoring is increasingly being integrated into geriatric care, enabling continuous assessment of behavioral and cognitive patterns that can detect early cognitive changes. This editorial examines key ethical and governance challenges associated with these tools, including the epistemic opacity of machine-learning models, distributed clinical responsibility, dynamic consent for passive data collection, and the equitable performance of algorithms across diverse populations. It argues that addressing these challenges requires governance frameworks that clarify accountability, ensure interpretability, and protect patient autonomy while supporting clinical decision-making. By discussing these considerations, this piece provides a structured perspective on responsible innovation in AI-supported cognitive monitoring, advancing discourse on ethical integration of emerging digital tools in aging populations.

Indexed as

aging brainartifical intelligencecognitive assessmentdigital health awarenessethics in aiolder adult

Identifiers

PMID41694900
PMCPMC12906334

What OpenQuestion 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 OpenQuestion graph.