Digital twin in health care
Digital Twin Driven Intelligent Systems and Emerging Metaverse, Springer Nature, ss.209-231, 2023
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2023
- Doi Numarası: 10.1007/978-981-99-0252-1_10
- Yayınevi: Springer Nature
- Sayfa Sayıları: ss.209-231
- Anahtar Kelimeler: Artificial intelligence, Computer, Digital twin, Health, Health care, Machine LEARNING, Medicine, Personalized medicine, Science
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
The widespread usage of electronic health data has recently increased the use of computer systems and Artificial Intelligence in the medical field. Digital Twin (DT) implementations in health care show great promise. A large number of medical errors associated with diagnosis and treatment, poor resource use, inadequacies in workflow, and insufficient time for patients and physicians necessitate the use of such technologies. DT was described as "a living model of a physical entity or a system, which can constantly adapt to changes and predict the future of the corresponding physical response based on collected online data and information." It is expected that DT implementation will bring significant benefits in the field of health care. The DTs of humans have the capacity to collect and analyze physical and contextual data to improve the quality of life and increase wellness. These computer-based techniques can also detect lifestyles and predict potential health problems. Additionally, data on the environment, age, and emotional state can be collected and analyzed to holistically understand and describe a user's conditions. Although the healthcare sector is among the areas where the use of DT implementations is expected to be beneficial, the use of this technology is still premature in the field of health care and the management of patients. One of the future goals is the Human Digital Twin, which will perform real-time body analysis. Another implementation is to simulate the effects of certain drugs. Computer models increase the possibilities of diagnosis and treatment. In this way, future treatments will be organized based on accurate model predictions to improve health, not on mere instant health data. DTs enable the visualization of the virtual replica (twin) of the patient by analyzing huge amounts of data with new technologies such as AI. In this way, they offer individual treatment options, providing opportunities such as seeing the treatment results and the course of the disease. DT can show whether a medical therapy or device is suitable for the patient by simulating the dose and device response before selecting a particular treatment. Artificial Intelligence-based deep learning networks are expected to assist in interpreting medical images, dermatological lesions, retinal images, pathology slides, endoscopy, electrocardiograms, and facial and vital signs. In this chapter, the reasons for the use of DT in the field of health care, possible benefits and limitations, and some examples of the use of Artificial Intelligence, machine learning, and DT in various branches are presented.