SYSTEMATIC REVIEW OF AI-POWERED DECISION SUPPORT TOOLS IN OBSTETRIC EMERGENCY CARE
Bibliographic record
Abstract
Background: Obstetric and gynecological emergencies demand rapid, high-stakes decision-making, where delays or inaccuracies can lead to serious maternal and fetal outcomes. Artificial intelligence (AI)-powered decision support tools are emerging as promising adjuncts to aid clinicians under such critical conditions. Despite increasing interest, the clinical utility, accuracy, and safety of these technologies in emergency women’s health care remain underexplored and unstandardized, warranting a comprehensive synthesis of current evidence. Objective: This systematic review aims to evaluate the effectiveness, safety, and real-time decision-making accuracy of AI-powered decision support tools in the management of gynecological and obstetric emergencies. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across PubMed, Cochrane Library, Scopus, and Web of Science from database inception to 2024. Inclusion criteria encompassed randomized controlled trials, feasibility studies, qualitative research, and developmental studies evaluating AI applications in obstetric and gynecological emergencies. Data were extracted using a standardized form and assessed for bias using the Cochrane Risk of Bias tool and Newcastle-Ottawa Scale, depending on study design. Due to heterogeneity, a qualitative synthesis was performed. Results: Eight studies were included, encompassing feasibility, qualitative, and developmental research. AI tools demonstrated high concordance with clinician decisions in simulated obstetric emergencies, improved triage classification, and enhanced workflow efficiency. Clinicians highlighted the importance of transparency, personalization, and ethical considerations in AI adoption. However, most studies were small-scale or simulation-based, limiting generalizability. Conclusion: AI-based decision support systems show encouraging potential in obstetric emergency care by enhancing diagnostic accuracy and clinical efficiency. Nonetheless, the current evidence base is preliminary. Rigorous, real-world validation and ethical integration are essential for safe and effective implementation in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".