Endocrine Abstracts

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. AI systems perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI encompasses a variety of technologies, including machine learning, natural language processing, robotics, and computer vision, all aimed at creating systems capable of performing complex tasks autonomously. AI-driven technologies, including machine learning algorithms and predictive analytics, are poised to enhance the precision and efficiency of clinical decision-making and will revolutionise the diagnosis, treatment, and management of endocrine disorders in children. AI-powered diagnostic tools can interpret medical images and laboratory results with remarkable accuracy. For example, facial recognition technology can diagnose genetic conditions by identifying characteristic facial features. AI algorithms are also being used to diagnose thyroid disease, diagnose pituitary tumours, predict metabolic outcomes, identify growth disorders, anal and predict central pubertal precocity, facilitating timely interventions and reducing the need for more extensive testing. Additionally, AI chatbots support patient care by providing real-time information and answering queries, enhancing patient engagement and adherence to treatment plans. AI-driven clinical decision support systems assist endocrinologists by offering evidence-based recommendations, ensuring that each child receives the most effective and tailored care. Generative AI, a subset of artificial intelligence focused on creating new content from existing data, is revolutionizing medicine. By leveraging advanced algorithms and vast datasets, generative AI models can produce synthetic medical images, simulate complex biological processes, and generate personalised treatment plans. Despite the promising benefits, the implementation of AI in paediatric endocrinology must address inherent challenges. These include ethical considerations such as data privacy, algorithmic bias, and the need for continuous validation of AI systems. Bias in AI algorithms can lead to disparities in care, particularly for underrepresented populations. Collaborative efforts between clinicians, data scientists, and policymakers are essential to ensure the safe and effective deployment of AI technologies. As technology continues to evolve, the integration of AI into clinical practice promises a future where paediatric endocrine care is more precise, efficient, and accessible.