About this Research Topic
With recent developments in data curation, multiscale modeling, and high-performance computing, there have been concurrently increasing and promising developments of AI, machine learning, and deep learning algorithms with potential applications in a cancer patient digital twin. This Research Topic is intended to present state-of-the-art science, research, and development in cancer patient digital twins in recent years. Over the long term, we would like to see digital twin research efforts transform into a national synergy among all stakeholders. Guiding a future vision, efforts in the development of cancer patient digital twin technologies would lead to enhanced care and quality of life for millions of cancer patients worldwide.
In this themed article collection, we welcome original contributions from the scientific community in the format of research articles, perspectives, and reviews. Potential topics include, but are not limited to:
• High content, high speed, and high-quality multimodal data acquisition
• Data standards and labeling for AI/ML
• Real-world data integration, curation, and management
• Multi-omics data and biomarker discovery in cancer treatments
• Mathematical, statistical, and mechanistic modeling of organs and systems
• Modeling and simulations of time-series data
• Tumor microenvironment modeling and simulations
• Ethical and trustworthy AI
• Responsible and explainable AI for cancer care
• Security and privacy in clinical AI
• Natural language processing of electronic health records and clinical notes
• Treatment response simulation and prediction
• Applications of digital twin technologies in treatment development and clinical trials
• Deployment strategies of digital twin technologies in healthcare
• AI-assisted clinical decision support
• Mobile health for patient monitoring and intervention
• AI and bioinformatics for improved quality of care
• AI-aided diagnosis and early detection of cancers
• Pharmacokinetics-Pharmacodynamics model of drug-tumor interactions
• Physics-informed machine learning
• Human-in-the-loop AI
• Knowledge representation and extraction
Keywords: Digital Twin, predictive oncology, oncology, machine learning, Artificial Intelligence, NLP
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.