AI-driven biosensing technology for early disease detection: A comprehensive review

Background

Artificial intelligence (AI) is quickly changing the field of biosensing technologies to the point of early, real-time, and individualized disease detection and overcoming significant drawbacks of traditional biosensors, including noise interference, signal drift, and inability to process non-linear biological data.

Objective

This review examines the latest developments in AI-driven biosensing systems and assess their impact on diagnostic performance and clinical relevance.

Methods

This critical review focuses on recently published works (2021–2025) on AI-integrated biosensing platforms, such as optical, electrochemical, wearable, and nanomaterial-based platforms, and machine learning, deep learning, edge AI, multimodal data fusion, and federated learning.

 Results: CNNs and LSTMs, Transformers and ensemble learning are advanced AI models with better sensitivity, specificity, automatic extraction of features, and prediction accuracy of critical diseases like cancer, cardiovascular, infectious, neurological, and metabolic diseases. Explainable Artificial Intelligence (XAI), nano-enabled biosensors, digital twins, and the Internet of Medical Things (IoMT) are examples of emerging technologies and ideas that are improving real-time, dependable, customized, and privacy-preserving diagnostic systems.

Conclusion

The change to proactive and predictive healthcare is being facilitated by AI-enhanced biosensing. It has a good chance in early diagnosis, better treatment planning as well as the decrease in healthcare expenses. Nevertheless, there are still challenges such as lack of data, generalization of models, regulatory authorization, and moral issues.