Metadata about: Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10(−18)M), Femtomolar (10(−15)M) and Picomolar (10(−12)M) in comparison to conventional biosensors with sensitivity of nanomolar 10(−9)M and micromolar 10(−3)M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.

About: Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10(−18)M), Femtomolar (10(−15)M) and Picomolar (10(−12)M) in comparison to conventional biosensors with sensitivity of nanomolar 10(−9)M and micromolar 10(−3)M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.

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