About this Research Topic
The world is now witnessing the healthcare transformation through digital health that may include interoperable electronic health records, clinical sensors, digital twins, cloud computing, and more recently AI. Patients these days have access to plethora of medical information as well as their own health records when needed. Digitized patients’ health records are considered as an important asset for any clinical institution as they contain valuable information on how to treat the patients who suffer from similar symptoms in the record sets. Such accumulated medical data can be used to train AI for better diagnosis result. Human doctors’ limited knowledge often leads to misdiagnosis but well-trained AI with large set of health data may produce more accurate diagnosis than human correspondent. Patients can have access to such well-trained AI to monitor their health using wearable sensors or other portable sensors along with observed symptoms. Collected sensor data can be sent to their own digital twin for diagnosis. Such diagnosis and guideline or instruction can be provided with associated services, which collectively called digital twin as a service.
The goal of this Research Topic is to bring together a collection of papers that used digital health technologies in association with machine learning and AI along with digital twin as a service (DTaaS). Machine learning techniques may be used to train data set for AI and DTaaS as enabler for personal healthcare system that may not involve clinicians’ assistance. Patients may be able to utilize trained data set for their diagnosis using AI and DTaaS. DTaaS comes with services that are needed to provide such monitoring and diagnosis. The internet of things using clinical sensors and observed symptoms are used to synchronize patients’ body condition with their own digital for monitoring personal health and diagnosis.
We welcome the submission of manuscripts including, but not limited to, the following topics:Special focus will be given (but is not restricted) to: digital health technologies utilizing machine learning and AI
1. Implementation of digital twin as a service (DTaaS) for healthcare.
- Cloud based DTaaS architecture and service model
- Machine learning techniques for training disease related health data
- Utilization of AI for diagnosis and guidelines from existing AI model such as ChatGPT
2. Clinical sensor technology for cloud computing
- Integrated clinical sensor
- Synchronization technologies for incoming data for digital twin
- IoT networking and security for health data
3. Personal healthcare utilizing the above items 1 and 2
The goal of this Research Topic is to bring together a collection of papers that used digital health technologies in association with machine learning and AI along with digital twin as a service (DTaaS). Machine learning techniques may be used to train data set for AI and DTaaS as enabler for personal healthcare system that may not involve clinicians’ assistance. Patients may be able to utilize trained data set for their diagnosis using AI and DTaaS. DTaaS comes with services that are needed to provide such monitoring and diagnosis. The internet of things using clinical sensors and observed symptoms are used to synchronize patients’ body condition with their own digital for monitoring personal health and diagnosis.
We welcome the submission of manuscripts including, but not limited to, the following topics:Special focus will be given (but is not restricted) to: digital health technologies utilizing machine learning and AI
1. Implementation of digital twin as a service (DTaaS) for healthcare.
- Cloud based DTaaS architecture and service model
- Machine learning techniques for training disease related health data
- Utilization of AI for diagnosis and guidelines from existing AI model such as ChatGPT
2. Clinical sensor technology for cloud computing
- Integrated clinical sensor
- Synchronization technologies for incoming data for digital twin
- IoT networking and security for health data
3. Personal healthcare utilizing the above items 1 and 2
Keywords: digital health, clinical sensors, IoT, Digital twin as a service
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
