2nd Edition of the Public Health and Midwifery World Conference 2026

Biography

Dr. Irina Koyfman is a Nurse Practitioner, Doctor of Nursing Practice, and healthcare innovator with more than 25 years of clinical and executive experience. She comes to the midwifery community as a fellow nurse, grounded in the same patient-first values that anchor maternal and community health, and focused on one urgent question: how do we prepare the nursing and midwifery workforce to lead the AI tools entering everyday practice, rather than be led by them?

Dr. Koyfman is the founder of Affinity Expert, a healthcare consulting firm specializing in chronic care management, remote patient monitoring, and AI transformation. She is the founder of the #NursesforAI movement, which advocates for nurses and midwives to be decision-makers in how AI is developed and deployed, not just end users. She holds board certification in Artificial Intelligence in Medicine (ABAIM).

She is the co-author of AI Competency Domains for Nurses: A Framework for Education, Practice, and Leadership, the framework underlying today's keynote, and author of The Sandwich Generation Handbook and AI Health Companion. Her work has been featured in The American Journal of Managed Care, the Journal of the American Association of Nurse Practitioners, Home Healthcare Now, and The Maryland Nurse Journal.

Abstract

Background: Artificial intelligence is entering maternal and community health faster than the workforce is being prepared to use it. Predictive risk scores, fetal monitoring algorithms, ambient documentation, and triage chatbots now sit inside everyday midwifery practice. Yet adoption is treated as a technology problem, when in fact it is a trust problem. Clinicians either over-rely on tools they do not understand or reject them outright. Both responses put patients at risk. Trust should not be a feeling that automation earns by default. It should be a teachable, assessable professional competency.

Aim:This presentation reframes trust as a core competency and offers a practical framework that prepares midwives and nurses to question, verify, and lead the AI tools used in maternal care, rather than passively consume their output.

Approach: Drawing on an established nursing AI competency framework and current evidence on automation bias and algorithmic safety, the framework defines what trustworthy engagement looks like at the bedside and maps each capability to a concrete maternal health touchpoint, from intake risk scoring to postpartum follow-up.

The Competency Domains

• Question: recognizing what an AI tool can and cannot do, and identifying when its recommendation conflicts with clinical judgment.
• Verify: checking AI output against the patient in front of you, spotting automation bias, and knowing when to override.
• Lead: advocating for safe implementation, surfacing bias that harms underserved mothers, and giving frontline feedback that shapes procurement and policy.

Learning Objectives: After this session, attendees will be able to: (1) describe trust in AI as a measurable competency rather than an attitude; (2) apply a question, verify, and lead approach to AI tools in their own maternal or community health setting; and (3) identify their role as the human safeguard against algorithmic error and bias.

Conclusion: Safe AI in maternal care will not come from better algorithms alone. It will come from a workforce equipped to challenge them. Positioning trust as a competency gives nursing and midwifery leaders a concrete, transferable way to protect patients while embracing innovation.