About this Research Topic
The advent of Artificial Intelligence (AI) in the healthcare field has led to innovative approaches in processing and interpreting the wealth of data inherent in orthopedic practice. From clinical records, radiological imaging, genomic profiles to patient-reported outcomes, these myriad, multi-faceted data sources present an invaluable opportunity for refining diagnostic and therapeutic strategies.
This Research Topic, "Artificial Intelligence-Based Multimodal Prediction Modeling in Orthopedic Surgery", seeks to collate a series of original research and review articles that illustrate the cutting-edge use of AI in harnessing multimodal data for predictive modeling across the diverse sub-specialties of orthopedics.
We particularly welcome contributions that delve into:
- The development and application of AI-driven models that leverage multimodal data to enhance diagnostic accuracy, prognostic prediction, and therapeutic decision-making in orthopedics.
- The ethical implications and practical challenges of integrating AI-based multimodal prediction models into everyday clinical practice, including data privacy and algorithmic transparency.
- Case studies or research showcasing the role of AI-based multimodal models in personalized patient care, including potential to improve surgical outcomes and patient satisfaction.
- Methods for addressing data scarcity challenges in AI research, including innovative strategies for data augmentation, synthetic data generation, and transfer learning in the context of orthopedic surgery.
By exploring these diverse aspects, we hope to shed light on the potential and limitations of AI-based multimodal prediction modeling, stimulating further research and collaboration in the field.
This Research Topic, "Artificial Intelligence-Based Multimodal Prediction Modeling in Orthopedic Surgery", seeks to collate a series of original research and review articles that illustrate the cutting-edge use of AI in harnessing multimodal data for predictive modeling across the diverse sub-specialties of orthopedics.
We particularly welcome contributions that delve into:
- The development and application of AI-driven models that leverage multimodal data to enhance diagnostic accuracy, prognostic prediction, and therapeutic decision-making in orthopedics.
- The ethical implications and practical challenges of integrating AI-based multimodal prediction models into everyday clinical practice, including data privacy and algorithmic transparency.
- Case studies or research showcasing the role of AI-based multimodal models in personalized patient care, including potential to improve surgical outcomes and patient satisfaction.
- Methods for addressing data scarcity challenges in AI research, including innovative strategies for data augmentation, synthetic data generation, and transfer learning in the context of orthopedic surgery.
By exploring these diverse aspects, we hope to shed light on the potential and limitations of AI-based multimodal prediction modeling, stimulating further research and collaboration in the field.
Keywords: Orthopedic Surgery, Artificial Intelligence, Multimodal Prediction, Therapeutic Decision-Making, Personalized Patient Care
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.
