Since the abundance of health data is collected from numerous sources, including electronic health records (EHRs), medical imaging, genomic sequencing, wearables, and medical devices; therefore it has sparked tremendous global interest and opened opportunities to improve patients care. However, artificial intelligence (AI) represents a paradigm shift in healthcare. Nowadays, AI is leveraging big data to improve patient care by revealing disease patterns and assisting personalized treatment and care. AI tools have the potential to anticipate when a person is at risk of developing chronic diseases like cancer, dementia, and asthma. Moreover, AI-based automated tools can suggest preventive measures before patients get worse. The broader applications of AI have already started in predicting re-admission, cutting human errors, managing epidemics, and discovering potential drugs. Indeed, AI is contributing to better care outcomes and improving the productivity and efficiency of care delivery.
The goal of the Research Topic is to gather original research, reviews, and short communication on the trends and current applications of artificial intelligence models and big data for patients' care.
Potential topics of interest might include, but are not limited to the following:
• Evidence-based clinical practice.
• Artificial intelligence for personalized care (e.g. diagnosis, prognosis, and treatment).
• Identification of potential biomarkers for guiding targeted therapy.
• Harnessing big data to identify disease trends, reduce medication errors, and better patients’ outcomes.
Since the abundance of health data is collected from numerous sources, including electronic health records (EHRs), medical imaging, genomic sequencing, wearables, and medical devices; therefore it has sparked tremendous global interest and opened opportunities to improve patients care. However, artificial intelligence (AI) represents a paradigm shift in healthcare. Nowadays, AI is leveraging big data to improve patient care by revealing disease patterns and assisting personalized treatment and care. AI tools have the potential to anticipate when a person is at risk of developing chronic diseases like cancer, dementia, and asthma. Moreover, AI-based automated tools can suggest preventive measures before patients get worse. The broader applications of AI have already started in predicting re-admission, cutting human errors, managing epidemics, and discovering potential drugs. Indeed, AI is contributing to better care outcomes and improving the productivity and efficiency of care delivery.
The goal of the Research Topic is to gather original research, reviews, and short communication on the trends and current applications of artificial intelligence models and big data for patients' care.
Potential topics of interest might include, but are not limited to the following:
• Evidence-based clinical practice.
• Artificial intelligence for personalized care (e.g. diagnosis, prognosis, and treatment).
• Identification of potential biomarkers for guiding targeted therapy.
• Harnessing big data to identify disease trends, reduce medication errors, and better patients’ outcomes.
