The unprecedented rise of artificial intelligence (AI) over the past few decades has fundamentally transformed how we navigate our daily existence. From the moment we wake, our home smart devices use AI to inform us of current news and reminders. Our phones use AI to auto-correct messages, and the Global Positioning System in our car allows real-time traffic predictions. Just as AI can help with simple human tasks, we now understand how it can become integrated within medicine to assist with clinician tasks.

AI is a multidisciplinary field that combines computer science, mathematics, neuroscience, and philosophy to learn from large datasets and provide prediction.1 AI medical systems have become capable of performing at or even surpassing human clinicians in many instances, thus, the Food and Drug Administration has approved several AI-assisted devices/interventions to be integrated with medicine.2 More specifically, integrating AI into neonatal care can lead to significant advancements in monitoring and early detection abilities.3

While AI is a broad term, there are distinct subgroups. Machine Learning (ML) is a subset of AI where learning from data exists without explicit programming.4 ML is further stratified into supervised learning and unsupervised learning. Supervised ML is commonly used for prognostic predictions to determine whether a patient responds to treatment or is at risk of clinical deterioration.5,6 Unsupervised learning allows for clustering and labeling of data without explicit training. In addition to ML, deep learning (DL) is another subset of AI that focuses on multi-layered neural networks that excel at learning complex patterns from large amounts of data.7 More recently, generative AI has emerged with the development of large language models (LLMs) (including OpenAI’s ChatGPT8) and has demonstrated the capacity for reasoning, with examples including the potential for LLMs to pass medical licensing exams.9

In contemporary healthcare, there exists a demand for sophisticated diagnostic methodologies, early detection systems, and precision monitoring tools to facilitate more accurate diagnoses and innovative therapeutic approaches that minimize invasive interventions. Through its application, AI can enhance healthcare delivery by optimizing diagnostic precision, forecasting disease trajectories, and illuminating previously undiscovered pathophysiological mechanisms underlying various medical conditions (Fig. 1).10 This synergistic integration of human expertise and AI not only improves clinical decision-making, but also paves the way for personalized medicine tailored to individual patient profiles.