About this Research Topic
The field of orthopedic surgery has seen a significant influx of data from various sources such as clinical records, radiological imaging, genomic profiles, and patient-reported outcomes. This wealth of data, while invaluable, presents a challenge in terms of processing and interpretation. The advent of Artificial Intelligence (AI) in healthcare has opened up new avenues for leveraging this data to refine diagnostic and therapeutic strategies. However, there are still gaps in the understanding and application of AI in orthopedics, particularly in the use of multimodal data for predictive modeling.
The primary aim of this Research Topic is 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. The goal is to enhance diagnostic accuracy, prognostic prediction, and therapeutic decision-making in orthopedics through the development and application of AI-driven models. Additionally, the Research Topic seeks to address the ethical implications and practical challenges of integrating AI-based multimodal prediction models into everyday clinical practice.
The scope of this Research Topic is defined by its focus on AI-based multimodal prediction modeling in orthopedic surgery. We welcome articles addressing, but not limited to, the following themes:
• The development and application of AI-driven models that leverage multimodal data in orthopedics;
• The ethical implications and practical challenges of integrating AI-based multimodal prediction models into clinical practice;
• Case studies or research showcasing the role of AI-based multimodal models in personalized patient care;
• Methods for addressing data scarcity challenges in AI research, including strategies for data augmentation, synthetic data generation, and transfer learning in orthopedic surgery.
The primary aim of this Research Topic is 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. The goal is to enhance diagnostic accuracy, prognostic prediction, and therapeutic decision-making in orthopedics through the development and application of AI-driven models. Additionally, the Research Topic seeks to address the ethical implications and practical challenges of integrating AI-based multimodal prediction models into everyday clinical practice.
The scope of this Research Topic is defined by its focus on AI-based multimodal prediction modeling in orthopedic surgery. We welcome articles addressing, but not limited to, the following themes:
• The development and application of AI-driven models that leverage multimodal data in orthopedics;
• The ethical implications and practical challenges of integrating AI-based multimodal prediction models into clinical practice;
• Case studies or research showcasing the role of AI-based multimodal models in personalized patient care;
• Methods for addressing data scarcity challenges in AI research, including strategies for data augmentation, synthetic data generation, and transfer learning in orthopedic surgery.
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.
