The Role of Artificial Intelligence in Accessing Quality Maternal Health and Addressing Gaps in the Service Delivery: Minireview
Bibliographic record
Abstract
Maternal health services encompass a range of care from preconception to postnatal care, including antenatal care and emergency obstetric care. Despite global awareness and efforts to improve these services, significant gaps remain. This paper seeks to provide wider understanding on the benefits and challenges of integrating Artificial intelligence to improve maternal health care services. It also identifies significant areas where Artificial intelligence is of utmost important in monitoring maternal health. To understand the perspective of how AI can be leveraged to achieve the Sustainable Development Goal 3 which targets the reduction of maternal mortality to less than 70 per 100000 births, this paper explored the possible services that can be rendered remotely for monitoring pregnant women to detect pregnancy complications and emergencies, the services that support health facility deliveries and postnatal care. The barriers to the effectiveness of AI in maternal health such as data privacy, cost effectiveness, data inaccuracy and ethical consideration are highlighted in this study. Also, the potential solutions to the challenges of integrating AI were also discussed. This demands for persistent and continuous actions to improve outcome of maternal health care services.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".