Healthcare cognitive assistants (HCAs) are intelligent systems or agents that interact with users in a context-aware and adaptive manner to improve their health outcomes by augmenting their cognitive abilities or complementing a cognitive impairment. They assist a wide variety of users ranging from patients to their healthcare providers (e.g., general practitioner, specialist, surgeon) in several situations (e.g., remote patient monitoring, emergency response, robotic surgery). While HCAs are critical to ensure personalized, scalable, and efficient healthcare, there exists a knowledge gap in finding the emerging trends, key challenges, design guidelines, and state-of-the-art technologies suitable for developing HCAs. This survey aims to bridge this gap for researchers from multiple domains, including but not limited to cyber-physical systems, artificial intelligence, human-computer interaction, robotics, and smart health. It provides a comprehensive definition of HCAs and outlines a novel, practical categorization of existing HCAs according to their target user role and the underlying application goals. This survey summarizes and assorts existing HCAs based on their characteristic features (i.e., interactive, context-aware, and adaptive) and enabling technological aspects (i.e., sensing, actuation, control, and computation). Finally, it identifies critical research questions and design recommendations to accelerate the development of the next generation of cognitive assistants for healthcare.

中文翻译：

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医疗保健认知助手综述

医疗保健认知助理 (HCA) 是智能系统或代理，它们以情境感知和自适应方式与用户交互，通过增强他们的认知能力或补充认知障碍来改善他们的健康结果。它们在多种情况下（例如，远程患者监测、紧急响应、机器人手术）为从患者到其医疗保健提供者（例如，全科医生、专家、外科医生）的各种用户提供帮助。虽然 HCA 对于确保个性化、可扩展和高效的医疗保健至关重要，但在寻找适合开发 HCA 的新兴趋势、关键挑战、设计指南和最先进的技术方面存在知识差距。这项调查旨在为来自多个领域的研究人员弥合这一差距，包括但不限于网络物理系统，人工智能、人机交互、机器人技术和智能健康。它提供了 HCA 的全面定义，并根据现有 HCA 的目标用户角色和底层应用程序目标，对现有 HCA 进行了新颖实用的分类。该调查根据现有 HCA 的特征（即交互式、上下文感知和自适应）和支持技术方面（即传感、驱动、控制和计算）对现有 HCA 进行了总结和分类。最后，它确定了关键的研究问题和设计建议，以加速下一代医疗保健认知助手的开发。根据目标用户角色和底层应用程序目标对现有 HCA 进行实际分类。该调查根据现有 HCA 的特征（即交互式、上下文感知和自适应）和支持技术方面（即传感、驱动、控制和计算）对现有 HCA 进行了总结和分类。最后，它确定了关键的研究问题和设计建议，以加速下一代医疗保健认知助手的开发。根据目标用户角色和底层应用程序目标对现有 HCA 进行实际分类。该调查根据现有 HCA 的特征（即交互式、上下文感知和自适应）和支持技术方面（即传感、驱动、控制和计算）对现有 HCA 进行了总结和分类。最后，它确定了关键的研究问题和设计建议，以加速下一代医疗保健认知助手的开发。