Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities

Abstract

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

Fig. 1. Overview and neonatology specific examples of a systematic data quality framework.

This flowchart depicts the key phases in an end-to-end data quality process. It begins with initial data acquisition in the Data Collection phase from sources such as electronic medical records, medical devices, research databases, and literature reviews. The next Data Processing stage involves activities such as data inspection, anomaly detection, cleaning, transformation, and integration to curate the dataset. The processed quality data is then stored and managed in the Data Storage phase. Subsequently in the Data Analysis phase, statistical analyses and visualizations are performed to derive insights and identify data quality issues to refine the overall collection and processing. Effective data governance and metadata management are critical throughout each phase to ensure accuracy, transparency, and reproducibility. The systematic workflow promotes high quality data essential for robust analytics and decision making in healthcare applications. We hypothesize that the importance of addressing these data quality challenges is amplified when using ML methods over traditional statistical methods, but this hypothesis has not been tested. Thus, we highlight these challenges in the context of ML for neonatal care, however, most can be applied to any analysis in any population.

Fig. 2. Stages of the AI/ML development lifecycle in neonatology, highlighting important ethical considerations.

The development of AI/ML models in healthcare follows a cyclical workflow that includes design, implementation, testing, deployment, monitoring, and retraining. Oversight by diverse stakeholders, nationally and locally, is critical for governance. This includes continuous risk reviews, monitoring adherence to principles, eliciting input through advisory boards, and enabling external auditing and clinician feedback. At each stage, unique ethical challenges arise that must be proactively addressed. In the design phase, considerations such as testability, usability, safety, bias, fairness, transparency, and interpretability should be prioritized from the start. Representative and unbiased data collection is crucial during implementation, along with privacy and security protections. Throughout testing, model performance and safety should be rigorously evaluated across diverse groups. Monitoring performance post-deployment enables continuous improvement through retraining. Overall, ethics should not be an afterthought but instead integrated into every step. The figure emphasizes that thoughtful design and testing parameters, mitigating bias and lack of equity, and ensuring comprehensibility for clinicians can promote better, more ethical AI in neonatology. However, this requires extensive collaboration between computer scientists, clinicians, and ethics experts across the entire development lifecycle.