Evidence map›Paper›PMID 41982626›Full record

ArticleFrontiers in digital health2026

Ethical examination of AI coaches: privacy, bias, and responsibility.

Wei Li, Liang Bian, Wei Wu

Abstract read
In one paragraph

Article in Frontiers in digital health, 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.

Wei LiSchool of Marxism Studies, Shanghai University of Sport, Shanghai, China.
Liang BianSchool of Marxism, Shanghai University of Finance and Economics, Shanghai, China.
Wei WuAcademy of Marxism, Nanjing University of Finance and Economics, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) into sports, particularly through AI-driven coaching systems, marks a transformative advancement with the potential to revolutionize personalized training. AI coaches can create customized, data-driven training programs designed to optimize athletic performance. However, this technological progress also brings with it significant ethical concerns, including privacy violations, data biases, and ambiguous responsibility in cases of failure or misuse. These risks extend beyond societal norms, posing threats to fundamental personal rights and raising questions about the fairness of athletic competitions. For example, privacy breaches could expose sensitive athlete data, while biases in training algorithms may create unfair advantages or disadvantages. Moreover, the lack of clear accountability for AI-related failures may lead to difficult legal and ethical dilemmas. To address these challenges, it is crucial to implement robust ethical safeguards. These safeguards should prioritize enhanced privacy protections, ensure equitable data collection and processing, and establish clear guidelines for the allocation of responsibility. By implementing such measures, AI coaches can be developed in a way that is ethically responsible and socially beneficial, thereby maximizing their potential to positively impact sports training.

Indexed as

AI coachethical risksfairnesspersonalized trainingsport

Identifiers

PMID41982626
PMCPMC13070764

What OpenQuestion 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 OpenQuestion graph.