Artificial Intelligence (Ai) in Medicine and Endocrinology Publisher



Sharghi S ; Larijani B
Source: Interdisciplinary Advances in Endocrinology Published:2025

Abstract

Artificial intelligence (AI) is broadly defined as the application of computational systems to simulate human-like intelligence, including reasoning, learning, and decision-making. In the medical domain, AI’s origins trace back to the 1970s, although early implementations were constrained by limited computational capacity, suboptimal algorithms, and insufficient data availability. These limitations significantly hindered progress and delayed its integration into clinical practice. The advent of deep learning in the early 2000s catalyzed a paradigm shift. With enhanced computational power, larger datasets, and improved algorithmic sophistication, many of the earlier obstacles were mitigated. Over the past two decades, AI has evolved rapidly, with systems now capable of autonomously analyzing large-scale, multidimensional datasets and adapting through iterative self-learning processes. These capabilities have paved the way for AI’s integration into various aspects of clinical care, including risk stratification, diagnostic accuracy enhancement, and optimization of clinical workflows. This transformative era is marked by promising outcomes in disease prediction, diagnosis, and management. AI contributes to the development of personalized treatment strategies, accelerates drug discovery processes, facilitates predictive analytics, and expands the scope of telemedicine. These innovations are progressively reshaping the delivery of healthcare. In endocrinology, AI-driven tools have demonstrated utility in diagnosing and managing a range of endocrine disorders, including diabetes mellitus, thyroid malignancies, and osteoporosis. Machine learning models, for instance, have shown promise in early disease detection, glycemic control prediction, and stratification of thyroid nodules on imaging. Looking forward, the application of AI in endocrine and metabolic research is expected to yield significant advancements in precision medicine, patient monitoring, and therapeutic efficacy. These developments hold the potential to reduce healthcare costs and enhance system-wide operational efficiency. Nonetheless, substantial challenges remain. Regulatory oversight and clinical validation are essential to ensure that AI-driven interventions are safe, effective, and equitably distributed. Moreover, the ethical implications of AI deployment in medicine—particularly issues related to data privacy, algorithmic bias, and equitable access—require continued scrutiny. Crucially, while AI serves as a powerful adjunct, it cannot substitute the clinical acumen, empathy, and ethical judgment inherent to human practitioners. The physician’s role remains indispensable, particularly in contexts requiring nuanced interpretation, compassion, and patient-centered care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.