Next‐Gen Midwifery Support: Designing an Artificial Intelligence ( <scp>AI</scp> ) Enhanced Mobile App for Pregnancy Risk Categorization and Clinical Decision Support on Maternal and Neonatal Outcomes
Bibliographic record
Abstract
BACKGROUND: Limited medical professionals, particularly in rural community, impedes patient treatment. Rapid prenatal risk assessments are critical for improving pregnancy care under these resource constraints. OBJECTIVE: To develop and evaluate an innovative digital system that assists midwives in recognizing prenatal risks and in making clinical decisions in maternity hospitals, especially in rural healthcare setups. METHODS: The technology, which is based on a smartphone application, assesses pregnancy risks and offers potential delivery insights. Researchers used data gathering, firebase integration, and an artificial intelligence model to perform a pilot study in rural health setups. The modified Alberta perinatal risk score is used and validated. Midwives are trained in the app's use and screened 1010 pregnant women at a primary health centres (PHC). RESULTS: Prenatal mother's data is securely maintained in JSON format, allowing for predictive evaluations of outcomes and intrapartum factors. The AI processes data and generates predictions for the Flutter App. Pilot results show that the app is effective at classifying prenatal cases, with 37.33% classified as low risk, 37.82% as intermediate risk, and 24.85% as high risk. High-risk cases are referred to facility-based centers, and midwives collaborated with medical officers to manage 62.04% of moderate and all low-risk cases. The app efficiently records maternal and neonatal outcomes, demonstrating its potential to improve patient care with a 99.0% accuracy rate in forecasting newborn fatalities using the Gradient Boost algorithm. CONCLUSIONS: An integrated android application with the AI antenatal risk assessment system improves midwives' obstetric risk assessment skills, allowing them to provide timely interventions to pregnant women, thus contributing to positive birthing outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".