Speeding up and improving the diagnosis process exactly where and when events occur is the goal of an actual Point of Care (PoC) Diagnostics. Besides progress in sensing technologies that pertains to multidisciplinary domains, including nanotechnologies, microfluidics and advanced materials, it is envisaged that PoC Diagnostics can significantly benefit from a tighter interplay with Artificial Intelligence.
Indeed, Artificial Intelligence and machine learning can lead to methods for integrating, analyzing and understanding multimedia data from a plethora of different devices. In addition, multivariate methods can correlate the current patient status with the previous history, adapting the findings to the patient's personal history in line with a more personalized and adaptive approach to care and favoring a more accurate prediction of future status.
To this end, there is the need to explore several research directions in artificial intelligence and point of care diagnostics. From one side, artificial intelligence paradigms can be embedded into PoC testing devices, extending their capabilities and making possible analyses otherwise not viable, e.g. those including image analysis. This can bring to a convergence of pervasive computing and PoC diagnostics. Similarly, networks of local devices can be devised taking advantage of distributed artificial intelligence: wearable sensors and portable devices can communicate in an ecosystem, and their data can be cumulatively and coherently processed. Finally, artificial intelligence can be decentralized, also considering a cloud-based approach, extending the capabilities of PoC diagnostics all over the computational continuum.
For instance, with a timely decentralized survey, PoC may allow detection of anomalies that once integrated with previously collected data and anamnesis, with the further purpose of a quality check to use reliable data, can be classified by Artificial Intelligence methods. Immediately, the system can then alert the user and his caregivers.
Moreover, specific assistance networks can guarantee control and rescue over the territory. Even if the cost of PoC devices is high, it reduces indirect costs and saves lives.
The Research Topic would cover the following topics that include, but is not limited to:
• Sensorized devices
• Portable and wearable devices
• Smartphone applications
• Multimedia data
• Systems for monitoring sport and physical activities
• Digital assistance healthcare in fragiles
• Telemedicine systems
• 3D modeling and 3D fabrication for PoC
• Artificial Intelligence
• Recommendation systems
• Machine learning
• Explainability of AI in the PoC setting
• Privacy and security issues
• Data reliability
Speeding up and improving the diagnosis process exactly where and when events occur is the goal of an actual Point of Care (PoC) Diagnostics. Besides progress in sensing technologies that pertains to multidisciplinary domains, including nanotechnologies, microfluidics and advanced materials, it is envisaged that PoC Diagnostics can significantly benefit from a tighter interplay with Artificial Intelligence.
Indeed, Artificial Intelligence and machine learning can lead to methods for integrating, analyzing and understanding multimedia data from a plethora of different devices. In addition, multivariate methods can correlate the current patient status with the previous history, adapting the findings to the patient's personal history in line with a more personalized and adaptive approach to care and favoring a more accurate prediction of future status.
To this end, there is the need to explore several research directions in artificial intelligence and point of care diagnostics. From one side, artificial intelligence paradigms can be embedded into PoC testing devices, extending their capabilities and making possible analyses otherwise not viable, e.g. those including image analysis. This can bring to a convergence of pervasive computing and PoC diagnostics. Similarly, networks of local devices can be devised taking advantage of distributed artificial intelligence: wearable sensors and portable devices can communicate in an ecosystem, and their data can be cumulatively and coherently processed. Finally, artificial intelligence can be decentralized, also considering a cloud-based approach, extending the capabilities of PoC diagnostics all over the computational continuum.
For instance, with a timely decentralized survey, PoC may allow detection of anomalies that once integrated with previously collected data and anamnesis, with the further purpose of a quality check to use reliable data, can be classified by Artificial Intelligence methods. Immediately, the system can then alert the user and his caregivers.
Moreover, specific assistance networks can guarantee control and rescue over the territory. Even if the cost of PoC devices is high, it reduces indirect costs and saves lives.
The Research Topic would cover the following topics that include, but is not limited to:
• Sensorized devices
• Portable and wearable devices
• Smartphone applications
• Multimedia data
• Systems for monitoring sport and physical activities
• Digital assistance healthcare in fragiles
• Telemedicine systems
• 3D modeling and 3D fabrication for PoC
• Artificial Intelligence
• Recommendation systems
• Machine learning
• Explainability of AI in the PoC setting
• Privacy and security issues
• Data reliability
