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

Area of Science:

• Exploration of artificial intelligence (AI) applications in healthcare.
• Focus on machine learning (ML) and natural language processing (NLP) for knowledge capture.

Background:

• Midwifery care relies heavily on tacit, experiential knowledge often overlooked in policy.
• AI offers a pathway to formalize this knowledge, improving outcomes and policy recognition.

Purpose of the Study:

• To investigate AI's role in capturing tacit midwifery knowledge.
• To assess AI's potential to enhance midwifery practices and policies.
• To examine AI's facilitation of integrating experiential knowledge into evidence-based practice.

Main Methods:

• Systematic review of peer-reviewed literature from 2015-2025.
• Included studies on AI in healthcare, midwifery, and AI ethics.
• Focused on AI applications in clinical settings and midwifery education.

Main Results:

• AI can unlock tacit midwifery knowledge, especially in perineal trauma prevention and clinical decision-making.
• AI models offer real-time risk assessments and reduce clinical variability.
• Analysis of qualitative data from experienced midwives is key.

Conclusions:

• AI holds significant potential for advancing midwifery by formalizing tacit knowledge and improving decision-making.
• Addressing ethical concerns, data privacy, and algorithmic bias is essential for AI integration.
• Policy frameworks must support AI development for midwifery and provide necessary training for practitioners.