Abstract :
Artificial Intelligence (AI) has emerged as a transformative tool in modern healthcare, giving new capabilities for real-time study and prediction of physiological trends. Homeostasis, the dynamic management of internal biological factors such as blood pressure, glucose concentration, core temperature, and pH, is crucial to preserving health. Disruption in homeostatic homeostasis often serves as an early sign of pathological diseases. Conventional diagnostic procedures generally uncover diseases only after clinical symptoms show, resulting in delayed interventions. In contrast, AI-enabled solutions, through integration with biosensors and Internet of Things (IoT) devices, facilitate continuous monitoring and early recognition of small physiological anomalies. Machine learning (ML) and deep learning (DL) algorithms can find non-linear connections within multidimensional datasets, aiding in the early diagnosis of cardiovascular problems, diabetes mellitus, sepsis, and neurodegenerative diseases by examining patterns in vital signs and behavioral data. Despite these gains, difficulties exist, including data heterogeneity, algorithmic bias, and limitations in training datasets, especially in marginalized populations. The interpretability of complicated AI models remains a major challenge, often limiting clinical acceptance. Additionally, legislative limits and concerns over data privacy provide impediments to wider implementation. Methodological shortcomings, such as the lack of defined data gathering techniques and insufficient longitudinal research, limit the generalizability of AI findings. Future directions should focus on federated learning, individualized predictive models, and the development of explainable AI frameworks. This paper critically explores present applications, technical limits, and future opportunities, emphasizing AI’s promise to improve early disease diagnosis by monitoring homeostatic disruptions with precision and efficiency.
Keyword :
Artificial intelligence, Homeostasis, Early disease detection, Machine learning, Predictive healthcare.