Scoping Review Examines Explainable AI in Medical Multimodal Data
This scoping review investigates the application of explainable artificial intelligence (XAI) techniques specifically within the domain of medical multimodal data. The research aims to map the existing landscape of XAI methodologies being used to interpret complex medical datasets that combine various data types, such as imaging, genomics, and clinical records. By analyzing current research, the review seeks to identify the prevalent XAI approaches and their effectiveness in enhancing the transparency and interpretability of AI models in healthcare. The study highlights the growing need for XAI in medicine, where understanding the reasoning behind AI-driven diagnoses or treatment recommendations is crucial for clinical adoption and patient safety. The review categorizes the types of multimodal data used and the corresponding XAI methods applied, providing a structured overview of the field. It also identifies potential gaps in the current research and suggests directions for future investigation. The ultimate goal is to offer a comprehensive understanding of how XAI can be leveraged to build more trustworthy and understandable AI systems for medical applications, thereby facilitating better clinical decision-making and advancing medical research.
AI's increasing integration into medical diagnostics necessitates robust explainability frameworks. This review highlights the critical need for XAI to bridge the gap between complex AI outputs and clinical interpretability, fostering trust and enabling informed decision-making. As AI models process increasingly diverse multimodal data, ensuring transparency becomes paramount. Future research should focus on developing standardized XAI metrics and validation protocols tailored for medical applications. This will be essential for navigating regulatory hurdles and promoting the responsible deployment of AI in healthcare, ultimately aiming to improve patient outcomes and system efficiency within the next decade.
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