Artificial intelligence biosensor: challenges and prospects

Wearable sensors aim to address the limitations of centralized and reactive healthcare by providing individuals with insight into their own physiology. However, the relationship between disease and therapeutic platforms is very complex, making it difficult to analyze their output. Integrating AI can bridge this gap by using pattern analysis and classification algorithms to improve diagnostic and therapeutic accuracy. The future of AI-biosensors (AI wearable sweat biosensors, AI ingestible biosensors, AI glass biosensors, AI implantable biosensors, etc.) will mainly focus on AI diagnosis (where the diagnostic algorithm in the microprocessor can verify the sensor output and present diagnostic information), big data processing (using self-contained space for historical data and various necessary parameters of data storage, significantly improving the controller’s performance), and self-learning/adaptation (embedded microprocessors with advanced programming functions, allowing the AI-biosensor to reconstruct the structure and parameters according to certain behavioral criteria and have adaptive functions).