Statistical methods have long been used to explore the correlation of variables in populations; to understand the various factors that may contribute to the risk or outcomes of a particular disease. Recently, Artificial intelligence (AI) technology has been used to establish a personalized risk prediction model to further achieve the goal of patient-centered personalized medicine, a popular topic of clinical research and medicine. AI technology is used to process and analyze the owned data in an efficient and new way to obtain unprecedented insights to assist medical decision-making. We expect that artificial intelligence prediction models will continue to be explored, developed, and optimized to enhance population health value and social well-being.
In the context of infectious diseases, AI has been successfully used for detecting and tracking the progression of various infectious diseases, such as COVID-19, malaria and tuberculosis.
The contribution of AI to infectious diseases includes various aspects, the most common of which is the use of big data, machine learning or deep learning algorithms. These methods can be used to develop personalized prediction models to determine the risk and prognosis of a certain pathogen or the associated complications. Based on the individualized prognosis prediction model established for each patient, it can assist physicians in making clinical decisions in complex situations, and contribute to disease prevention and treatment, as well as joint decision-making between doctors and patients.
This Research Topic welcomes original research, reviews and case reports on the application of AI, Machine Learning or deep learning algorithms on (including but not limited to) the following themes:
• The diagnosis of infectious diseases
• The treatment of infectious diseases
• Progression of infectious disease in an individual.
• Transmission of infectious diseases between individuals and a population
• Clinical decision making for infectious diseases.
• Antimicrobial resistance
• Potential drug targets of pathogens.
Statistical methods have long been used to explore the correlation of variables in populations; to understand the various factors that may contribute to the risk or outcomes of a particular disease. Recently, Artificial intelligence (AI) technology has been used to establish a personalized risk prediction model to further achieve the goal of patient-centered personalized medicine, a popular topic of clinical research and medicine. AI technology is used to process and analyze the owned data in an efficient and new way to obtain unprecedented insights to assist medical decision-making. We expect that artificial intelligence prediction models will continue to be explored, developed, and optimized to enhance population health value and social well-being.
In the context of infectious diseases, AI has been successfully used for detecting and tracking the progression of various infectious diseases, such as COVID-19, malaria and tuberculosis.
The contribution of AI to infectious diseases includes various aspects, the most common of which is the use of big data, machine learning or deep learning algorithms. These methods can be used to develop personalized prediction models to determine the risk and prognosis of a certain pathogen or the associated complications. Based on the individualized prognosis prediction model established for each patient, it can assist physicians in making clinical decisions in complex situations, and contribute to disease prevention and treatment, as well as joint decision-making between doctors and patients.
This Research Topic welcomes original research, reviews and case reports on the application of AI, Machine Learning or deep learning algorithms on (including but not limited to) the following themes:
• The diagnosis of infectious diseases
• The treatment of infectious diseases
• Progression of infectious disease in an individual.
• Transmission of infectious diseases between individuals and a population
• Clinical decision making for infectious diseases.
• Antimicrobial resistance
• Potential drug targets of pathogens.
