ReviewCost effectiveness and resource allocation : C/E2026
The economic imperative of artificial intelligence in maternal and neonatal health: a review of evaluation benefits, frameworks, challenges, future perspectives, and limitations.
Review in Cost effectiveness and resource allocation : C/E, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Beyond the 'Pregnancy Black Box': a global roadmap for artificial intelligence-driven pharmacogenomics in maternal-neonatal health.The pharmacogenomics journal · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating artificial intelligence (AI) into maternal and neonatal health (MNH) offers significant opportunities for enhancing patient care through advanced predictive modeling, early disease diagnosis, and ongoing monitoring of conditions such as preeclampsia or gestational diabetes. However, significant challenges in economic valuation persist, including data scarcity, complexity, and the nascent stage of AI implementation in clinical practice. There has been no consolidated empirical proof directly justifying widescale AI application in MNH so far, despite its potentially significant economic benefits and direct cost savings. This review demonstrates that AI systems can mitigate adverse drug reactions (ADRs) and enhance the operational efficiency of organizations. As the full economic potential has yet to be understood and quantified, this review examines several existing economic evaluation frameworks: Cost-Effectiveness Analysis (CEA), Cost-Utility Analysis (CUA), Cost-Benefit Analysis (CBA), and Budget Impact Analysis (BIA). A crucial gap exists between rapid technological advancements and robust economic evaluations, further compounded by a lack of standardized reporting frameworks that hinder the synthesis of available evidence. In addition, the review addresses key challenges, including how they affect the healthcare workforce and the economic impact of systemic errors and security breaches, and then discusses the clinical and liability risks posed by "black box" models. Furthermore, the frequent updates essential for the clinical efficacy and safety of AI tools in MNH are often tied to subscription-based models, creating significant financial strain, particularly in low and middle-income-countries (LMICs). To bridge this crucial research gap and the absence of uniform reporting, this paper proposes the AI-MNH economic evaluation lifecycle and a tailored CHEERS checklist. This multi-phase framework is designed to guide comprehensive, long-term economic evaluations and the adoption of a consolidated, standardized approach to support evidence-based policymaking and sustainable resource allocation.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.