Evidence map›Paper›PMID 40781830›Full record

ReviewInternational nursing review2025

Artificial Intelligence and Tacit Knowledge Integration in Midwifery: Policy Implications for Improving Healthcare Outcomes.

Shoko Takeuchi, Kazumi Kubota, Sachiyo Nakamura

Abstract readReview
In one paragraph

Review in International nursing review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shoko TakeuchiDepartment of Nursing, Graduate School of Medicine, Yokohama City University, Yokohama, Kanagawa, Japan.ORCID https://orcid.org/0000-0003-2399-8179
Kazumi KubotaDepartment of Data Science, Shimonoseki City University, Shimonoseki City, Yamaguchi, Japan.ORCID https://orcid.org/0000-0003-2270-2313
Sachiyo NakamuraDepartment of Nursing, Graduate School of Medicine, Yokohama City University, Yokohama, Kanagawa, Japan.ORCID https://orcid.org/0000-0003-2028-6803

Funding

JSPS KAKENHI JP23K10109
6 · The paper itself

Abstract

aimTo explore the role of artificial intelligence (AI) in capturing tacit midwifery knowledge and its potential to enhance nursing and midwifery practices and policies, and examine how AI tools, such as machine learning (ML) and natural language processing (NLP), can facilitate the integration of midwives' experiential knowledge into evidence-based practice and policy.

backgroundMidwifery is largely driven by tacit knowledge gained through personal experience. This knowledge, which is crucial to maternal and newborn care, is often underrecognized in clinical policies. AI presents an opportunity to formalize this knowledge, improve healthcare outcomes, and increase the visibility of midwifery within policy frameworks. SOURCES OF EVIDENCE: A review of recent peer-reviewed literature focusing on studies related to AI in healthcare, midwifery practices, and AI ethics was conducted. Thirty-one sources spanning 2015-2025 were reviewed, emphasizing AI applications in clinical settings and midwifery education. DISCUSSION: AI has the potential to unlock tacit midwifery knowledge, particularly in perineal trauma prevention and clinical decision-making. AI models can provide real-time risk assessments and reduce clinical variability by analyzing qualitative data from experienced practitioners. However, ethical concerns, such as data privacy and algorithmic bias, must be addressed in AI integration.

conclusionAI offers substantial potential for improving midwifery practices by formalizing tacit knowledge and enhancing decision-making. However, successful integration requires careful attention to the ethical, governance, and regulatory issues. IMPLICATIONS FOR NURSING PRACTICE AND POLICY: Policy frameworks should encourage the development of AI tools tailored to midwifery, ensuring that midwives are equipped with the necessary training to work with these systems.

Indexed as

Artificial IntelligenceHealth PolicyMidwiferyAdultFemaleHumansPregnancyAI literacyartificial intelligenceclinical decision supporthealthcare ethicsmidwiferyperineal trauma preventionpolicy integrationtacit knowledge

Identifiers

PMID40781830
PMCPMC12334866

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.