Artificial Intelligence in Disease Diagnosis
1Chinese Academy of Sciences, Beijing, China
2Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
3First Affiliated Hospital of Huzhou University, Nanjing, China
4Institute for Infocomm Research, A*STAR, Singapore
Artificial Intelligence in Disease Diagnosis
Description
Accurate diagnosis of diseases is crucial for treatment planning and ensuring the well-being of patients. Human errors limit the diagnosis accuracy and efficiency, especially in general clinical practice and rural areas, as interpreting medical knowledge is a complex and cognitively challenging task. Artificial intelligence (AI) techniques, such as convolutional neural networks, knowledge graphs, and transformers, are validated as powerful and promising tools to assist and improve the diagnosis and even treatment of various diseases. The application of AI within the diagnostic process supports medical specialists to improve the level of diagnostic accuracy and efficiency, thus providing emergent digitalized healthcare services.
However, significant challenges remain in terms of reliability and validation, number and type of conditions considered, cross-modality analysis, novel examination technologies, and the effective transfer of advanced computer vision and machine learning technologies.
This Special Issue focuses on artificial intelligence research and applications for disease diagnosis. We welcome original research and review articles.
Potential topics include but are not limited to the following:
- Artificial intelligence tools for healthcare management
- Artificial intelligence applications for clinical decision support
- Novel artificial intelligence theory and algorithm
- Disease biomarker identification for systemic conditions
- Clinical validation of artificial intelligence applications for disease diagnosis
- Combined analysis of multiple organs using artificial intelligence
- Knowledge graphs for healthcare
- Medical image analysis for computer-aided diagnosis
- Multimodal fusion for disease detection and treatment
- Computer-assisted surgery
- New public datasets and baselines