Endocrine Abstracts

Artificial Intelligence (AI) is reshaping the landscape of paediatric endocrinology, offering novel computational tools to decode the multifactorial complexity of growth and pituitary disorders. These conditions often present with subtle, overlapping phenotypes and require longitudinal data interpretation, an area where AI excels. By leveraging large-scale datasets encompassing growth trajectories, biochemical markers, neuroimaging, and genetic profiles, machine learning algorithms are now capable of identifying diagnostic signatures with a level of precision that surpasses traditional clinical heuristics. Predictive analytics are increasingly informing therapeutic decisions, particularly in growth hormone therapy. Algorithms trained on real-world treatment data can forecast response trajectories, identify likely non-responders, and support dynamic dose titration, thereby enhancing both efficacy and safety. Machine learning algorithms trained on multimodal datasets including hormonal profiles, MRI imaging, growth velocity data, and genetic markers, are capable of identifying diagnostic signatures of conditions such as growth hormone deficiency, hypopituitarism, and craniopharyngioma-related dysfunction with high sensitivity and specificity. These models can detect subtle deviations in pituitary morphology and function that may precede overt clinical symptoms, enabling earlier recognition and stratification. Overall, as federated learning and synthetic data generation mature, AI systems are poised to overcome data scarcity and heterogeneity. AI-driven insights are facilitating a shift toward precision endocrinology, where care is tailored to individual biological and contextual profiles. However, the integration of AI into paediatric practice brings challenges. Data heterogeneity, limited paediatric-specific training sets, and algorithmic bias pose risks to generalisability and equity. Rigorous validation, transparent model governance, and inclusive data sourcing are essential to mitigate these concerns. Moreover, embedding AI into clinical workflows demands interoperability, clinician trust, and multidisciplinary collaboration. Looking ahead, innovations such as federated learning, AI-assisted imaging, generative AI, quantum AI-fusion, synthetic data generation, and AI-enabled registries hold promise for advancing research and care in rare endocrine conditions and improving population-level surveillance. The convergence of clinical expertise and computational intelligence is poised to redefine diagnostic paradigms, therapeutic strategies, and long-term outcomes in paediatric growth and pituitary disorders, heralding a new era of data-driven, child-centred endocrine care.