Introduction:
The integration of artificial intelligence (AI) sensors in healthcare has the potential to considerably improve patient monitoring, diagnosis, and treatment. However, the opaque nature of many AI systems known as black boxes poses significant challenges regarding transparency, explainability, and trustworthiness, which are critical factors in health delivery and management. This research aims to develop a framework for designing and deploying explainable and transparent AI sensors in healthcare.
Methods:
Through a comprehensive literature review and empirical analysis, we identify the key requirements and challenges associated with developing transparent and explainable AI (XAI) systems for healthcare applications. Our proposed approach combines interpretable machine learning models, human-AI interaction mechanisms, and ethical guidelines to ensure that AI sensor outputs are comprehensible, auditable, and aligned with clinical decision-making processes.
Results and Discussion:
Our proposed framework encompasses three core components: (1) an interpretable AI model architecture that leverages techniques such as attention mechanisms, symbolic reasoning, and rule-based systems to provide human-understandable explanations; (2) an interactive interface that facilitates effective communication and collaboration between healthcare professionals and AI systems, enabling seamless integration of AI insights into clinical workflows; and (3) a robust ethical and regulatory framework that addresses issues of bias, privacy, and accountability in the deployment of AI sensors in healthcare. Through case studies and simulations, we demonstrate the efficacy of our approach in enhancing transparency, explainability, and trust in AI-powered healthcare applications.
Conclusions:
The proposed framework contributes to the responsible development of AI technologies and paves the way for improved patient outcomes, informed decision-making, and increased public acceptance of AI in healthcare. By addressing the challenges of transparency and explainability, our research facilitates the safe and ethical adoption of AI sensors in healthcare.