Abstract
Biochemical sensing plays a vital role in the research of life and natural science. However, with the increase of environmental complexity and amount of sample, traditional sensors cannot meet the needs of accurate and efficient analysis of numerous data. The emergence of AI provides a new strategy for overcoming the challenges in biochemical sensing, especially in the field of molecular diagnosis and imaging analysis. In this review, we focus on how AI reinforce biochemical sensing in accuracy, sensitivity, specificity, and efficiency. The applications of AI in material synthesis, molecular diagnosis and imaging analysis in the past three years are reviewed, and the characteristics and performance evaluation methods of the algorithms are emphatically discussed. Finally, we proposed the challenges of AI biochemical sensing in application, including database construction, reasonable selection of algorithms, and data reliability verification, and clarified the advancements needed and future development prospects.
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