Evidence map›Paper›PMID 41326301›Full record

ArticleThe journal of prevention of Alzheimer's disease2025

AI models, bias and data sharing efforts to tackle Alzheimer's disease and related dementias.

Vijaya B Kolachalama, Vijay Sureshkumar, Rhoda Au

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The journal of prevention of Alzheimer's disease, 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

3 authors.

Vijaya B KolachalamaDepartment of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA, 02118; Department of Computer Science, Boston University, MA, USA, 02215; Faculty of Computing & Data Sciences, Boston University, MA, USA, 02215. Electronic address: vkola@bu.edu.
Vijay SureshkumarGates Ventures, Seattle, WA, USA, 98033.
Rhoda AuDepartment of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA, 02118; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA, 02118; The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA, 02118; Departments of Anatomy & Neurobiology and Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI), often seen as a harbinger of future innovation, also presents a dilemma: it can perpetuate existing human biases. However, this issue is not novel or unique to AI. Humans have long been the progenitors of biases, and AI, as a product of human creation, often mirrors these inherent tendencies. Here, we present a perspective on the development and use of AI, recognizing it as a tool influenced by human input and societal norms, rather than an autonomous entity. Modern efforts to technologically enabled data collection approaches and model development, particularly in the context of Alzheimer's disease and related dementias, can potentially reduce bias in AI. We also highlight the importance of data sharing from existing legacy cohorts to help accelerate ongoing AI model development efforts for greater scientific good and clinical care.

Indexed as

(AI)Artificial intelligenceBiasData sharing

Identifiers

PMID41326301
PMCPMC12811759

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.