Perspective on AI-Driven High-Risk Pregnancy Prediction and Ethical Safeguards

A perspective article by Qingqing Ji, published in March 2026, examines the ethical implications of AI for perinatal risk prediction. The document integrates evidence on AI-based obstetric tools, prenatal screening impacts, and principles for trustworthy digital health. It is a 18.3 KB DOCX file under a CC BY 4.0 license.

Use Cases

• Analyze the discussion of AI-based obstetric risk prediction models and their clinical data inputs for ethical AI research
• Extract proposed safeguards like transparent model documentation and fairness monitoring for policy guideline development
• Study the outlined discrimination pathways, including biased data and defensive clinical escalation, for bias auditing frameworks
• Use the integrated narrative on risk communication and psychological support to inform patient-centered digital health design

Strengths

• Published in March 2026, providing recent scholarly perspective
• Released under a permissive CC BY 4.0 license for broad reuse
• Integrates evidence from multiple domains: AI prediction, prenatal screening, and digital health governance

Limitations

• The 18.3 KB size indicates it is a short perspective document, not a dataset for quantitative analysis
• No structured data, columns, or sample records are provided for machine learning tasks
• Content is a narrative synthesis, not empirical data, limiting use to qualitative or conceptual research

Provenance

Source

figshare, author Qingqing Ji

Collection Method

Targeted, non-systematic narrative synthesis integrating multiple evidence sources

Time Range

Focuses on developments over the past five years prior to 2026

Freshness

Last updated March 2026.

Geography

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