Perspective on AI-Driven High-Risk Pregnancy Prediction

Qingqing Ji authored a perspective article examining AI-based obstetric risk prediction, published on figshare in March 2026. The 18.4 KB document synthesizes evidence on AI tools, prenatal screening impacts, and principles of trustworthy digital health. It analyzes how risk labeling may increase anxiety and discrimination, proposing integrated safeguards for future deployments.

Use Cases

• Analyze the discussion of biased data and labels over-flagging socially disadvantaged groups for fairness audits in AI development.
• Extract proposed safeguards like transparent model documentation and continuous fairness monitoring for ethical AI framework design.
• Reference the synthesis of evidence on AI-based obstetric risk prediction and prenatal screening for literature reviews on digital health.

Strengths

• Published under a permissive CC BY 4.0 license for broad reuse.
• Authored by a named researcher, Qingqing Ji, providing clear attribution.
• Recently updated in March 2026, offering contemporary perspective.

Limitations

• The 18.4 KB file is a perspective article, not a quantitative dataset, limiting analytical applications.
• No structured data, columns, or sample data are provided for computational analysis.
• Based on a targeted, non-systematic narrative synthesis, not a comprehensive empirical study.

Provenance

Source

figshare

Collection Method

Targeted, non-systematic narrative synthesis of existing literature and principles.

Time Range

Perspective covering developments over the past five years.

Freshness

Last updated March 2026.

Geography

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