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Background: Trust is a critical driver of technology usage behaviors and is essential for technology adoption. Thus, nurses’ participation in software development is critical for influencing their involvement, competency, and overall perceptions of software quality. Purpose: To engage nurses as subject matter experts to develop a machine learning (ML) Pain Recognition Automated Monitoring System. Method: Using the Human-centered Design for Embedded Machine Learning Solutions (HCDe-MLS) model, nurses informed the development of an intuitive data labeling software solution, Human-to-Artificial Intelligence (H2AI). Findings: H2AI facilitated efficient data labeling, stored labeled data to train ML models, and tracked inter-rater reliability. OpenCV provided efficient video-to-image data pre-processing for data labeling. MobileFaceNet demonstrated superior results for default landmark placement on neonatal video images. Discussion: Nurses’ engagement in clinical decision support software development is critical for ensuring the end-product addresses nurses’ priorities, reflects nurses’ actual cognitive and decision-making processes, and garners nurses’ trust and technology adoption.