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

Abstract

AIM: To 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. BACKGROUND: Midwifery 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. CONCLUSION: AI 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.

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