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A Clinician-Centered Explainable Artificial Intelligence Framework for Decision Support in the Operating Theatre

Year: 2024

Type: conference-paper

Abstract: The integration of Artificial Intelligence (AI) into clinical decision support systems (CDSS) marks a significant advancement in the pursuit of enhanced patient care and operational efficiency in high-stakes environments, such as the operating room (OR) [1]. However, the complexity and often "black box" nature of these AI systems pose substantial challenges for human-AI teaming, where explainability is crucial for clinicians to understand and trust the AI's recommendations. The field of explainable Artificial Intelligence (xAI) offers promising methods and techniques to make AI decisions more explainable and interpretable, yet the adaptation of these methods to meet the specific needs of clinicians remains underexplored [2]. Our proposed clinician-centered framework, xAI-SURG, seeks to bridge this gap by aligning xAI approaches with the complex decision- making processes inherent in the OR. By evaluating the xAI-SURG framework through the lens of perfusionists' decision-making during cardiac surgery, we provide a use-case, showcasing the importance of tailored xAI that not only aligns with but also enhances clinician expertise and patient safety and outcomes in the OR.

Source: Hamlyn Symposium on Medical Robotics (HSMR)

DOI: 10.31256/hsmr2024.18