About this Research Topic
The rapid development of artificial intelligence (AI) technology has become a cornerstone of multidisciplinary research worldwide, establishing a new paradigm of "AI for Science." AI is progressively transforming the healthcare landscape, offering unprecedented solutions and playing an increasingly pivotal role in the prevention, diagnosis, and treatment of diseases. However, modern medical data processing faces numerous challenges, including the high complexity of data dimensions, data imbalance, privacy concerns, and the need to extract meaningful insights from vast amounts of information. This special issue aims to delve into the processing methods and applications of AI technologies in the realm of medical big data, to assist healthcare professionals and researchers in efficiently managing these data to enhance the accuracy of diagnosis, prediction, and decision-making.
To address these issues, multimodal learning should be employed to process multi-source heterogeneous data, with data augmentation and generative adversarial networks to handle data imbalance. Federated learning and homomorphic encryption technologies can be used to ensure data privacy, while deep neural networks can be applied for modeling and training complex data. Additionally, intelligent computing and uncertainty reasoning methods, such as multi-source information fusion theory, D-S evidence theory, rough set theory, possibility theory, genetic algorithms, and support vector machines, can also be leveraged.
The themes of interest for this Research Topic include but are not limited to:
1. AI can identify potential health risks by analyzing medical images, genetic data, medical records, and other information, and provide patients with personalized predictions and warnings.
2. AI can help analyze patients’ health data, discover disease patterns, optimize treatment plans, improve treatment effects, and reduce side effects. At the same time, AI can also be used in new drug research and development, shortening the research and development cycle and reducing costs.
3. With the popularization of smart hardware, AI is driving health management towards a more convenient and personalized direction. Smart wearable devices can monitor the user's physiological state in real time and provide users with customized health advice based on personal health data, helping patients and health managers better prevent diseases, recover, and manage their health over the long term.
To address these issues, multimodal learning should be employed to process multi-source heterogeneous data, with data augmentation and generative adversarial networks to handle data imbalance. Federated learning and homomorphic encryption technologies can be used to ensure data privacy, while deep neural networks can be applied for modeling and training complex data. Additionally, intelligent computing and uncertainty reasoning methods, such as multi-source information fusion theory, D-S evidence theory, rough set theory, possibility theory, genetic algorithms, and support vector machines, can also be leveraged.
The themes of interest for this Research Topic include but are not limited to:
1. AI can identify potential health risks by analyzing medical images, genetic data, medical records, and other information, and provide patients with personalized predictions and warnings.
2. AI can help analyze patients’ health data, discover disease patterns, optimize treatment plans, improve treatment effects, and reduce side effects. At the same time, AI can also be used in new drug research and development, shortening the research and development cycle and reducing costs.
3. With the popularization of smart hardware, AI is driving health management towards a more convenient and personalized direction. Smart wearable devices can monitor the user's physiological state in real time and provide users with customized health advice based on personal health data, helping patients and health managers better prevent diseases, recover, and manage their health over the long term.
Keywords: Medical Image Processing, Bioinformatics, Genomics, Electronic Health Record (EHR) Data Analysis, Natural Language Processing, Sensor Data Analysis
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