Acceptance of Remote Assistive Robots with and without Human-in-the-Loop for Healthcare Applications

Nertinger, Simone; Kirschner, Robin Jeanne; Naceri, Abdeldjallil; Haddadin, Sami

doi:10.1007/s12369-022-00931-9

Cited by 14 publications

(2 citation statements)

References 97 publications

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“…Today autonomous systems might not reach the required performances or human supervision is required together with the possibility of effectively operating in emergency actions. Remote control continues to be relevant in applications such as operating in hazardous environments [32] and medical applications [33,34]. Moreover, being humans good at performing several tasks, they can be also exploited to teach robots how to perform several tasks.…”

Section: Related Workmentioning

confidence: 99%

Human Control of Simulated Modular Soft Robots May Predict the Performance of Optimized AI-Based Controllers

Pietrosanti,

Nadizar,

Pigozzi

et al. 2023

IEEE Access

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Robots with a modular body permit a wide range of physical configurations, which can be obtained by arranging the composing modules differently. While this freedom makes modular robots capable of performing different tasks, finding the optimal physical configuration for a given task is not trivial. In fact, practitioners attempt to jointly optimize the body and the controller of the robot for a given task, but the result is not always satisfactory. More broadly, it is not clear what factors make a physical configuration more or less successful. In this paper, we aim to fill this gap and verify if humans can be predictive with respect to the performance of an optimized controller for a given robot body. We consider the case of Voxelbased Soft Robots (VSRs), whose rich dynamic induced by the softness of the modules makes the body particularly relevant for the robot ability to perform a task. We instantiate a number of (simulated) VSR bodies, differing in shape and actuation mechanism, and let a panel of humans control them, by means of online interaction with the simulator, while performing the task of locomotion. We use the same bodies with controllers obtained with evolutionary optimization, for the same task. We compare the ranking of human-and optimized AI-based VSRs, finding them very similar. We believe that our results strengthen the hypothesis that intrinsic factors in the body of modular robots determine their success.

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Section: Related Workmentioning

confidence: 99%

Human Control of Simulated Modular Soft Robots May Predict the Performance of Optimized AI-Based Controllers

Pietrosanti,

Nadizar,

Pigozzi

et al. 2023

IEEE Access

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“…artificial intelligence (AI) systems offer significant support in decision-making for end-of-life care in healthcare settings [39][40][41]. By leveraging advanced algorithms, machine learning, and data analytics, AI contributes to more informed, timely, and personalized decisions, ultimately improving the quality of care provided to individuals at the end of their lives.…”

Section: How Ai Technologies Contribute To End-of-life Decision-makin...mentioning

confidence: 99%

Ethical Considerations in AI-assisted Decision- Making for End-Of-Life Care in Healthcare.

Sivasubramanian Balasubramanian

2023

pst

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This paper delves into the ethical implications of deploying artificial intelligence (AI) in decision-making processes related to end-of-life care within healthcare settings. As AI continues to advance, its integration in healthcare introduces both opportunities and challenges, particularly in navigating the sensitive realm of end-of-life care. The paper explores this intersection, seeking to contribute valuable insights to the ongoing discourse on responsible AI implementation in the healthcare sector. Central to ethical considerations in end-of-life care is the principle of autonomy, emphasizing the importance of respecting patients' ability to make informed decisions about their care preferences. The paper argues for the need to design AI systems that augment rather than diminish patient autonomy, ensuring that individuals facing end-of-life decisions remain active participants in the process. Furthermore, the principles of beneficence and non-maleficence are highlighted, emphasizing the imperative for AI systems to enhance patient well-being while minimizing the risk of harm, both physical and psychological. Justice in the distribution of healthcare resources, including AI technologies, is crucial, and the paper emphasizes the need to address potential disparities in access. Transparent and explainable AI systems are advocated to foster trust among patients, families, and healthcare providers, enabling a better understanding of the rationale behind AI-driven recommendations. [1] The concept of accountability is explored, emphasizing the continued responsibility of healthcare professionals in overseeing and validating AI recommendations to maintain ethical standards. Cultural sensitivity is identified as a key consideration, recognizing and respecting diverse perspectives on end-of-life care. The paper underscores the significance of designing AI systems that accommodate cultural nuances and avoid imposing values that may conflict with patients' beliefs and preferences. Additionally, the emotional and psychological impact of AI-assisted decision-making is addressed, emphasizing the importance of maintaining the human touch in end-of-life care and acknowledging the roles of empathy, compassion, and human connection. The paper provides a comprehensive examination of the ethical dimensions surrounding AI-assisted decision-making in end-of-life care. By addressing autonomy, beneficence, justice, transparency, accountability, cultural sensitivity, and emotional impact, it offers a framework for responsible AI integration that aligns with ethical principles in healthcare, ultimately contributing to the enhancement of end-of-life care practices.

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A Concise Overview of Safety Aspects in Human-Robot Interaction

Hamad,

Nertinger,

Kirschner

et al. 2024

Springer Proceedings in Advanced Robotics

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Acceptance of Remote Assistive Robots with and without Human-in-the-Loop for Healthcare Applications

Cited by 14 publications

References 97 publications

Human Control of Simulated Modular Soft Robots May Predict the Performance of Optimized AI-Based Controllers

Human Control of Simulated Modular Soft Robots May Predict the Performance of Optimized AI-Based Controllers

Ethical Considerations in AI-assisted Decision- Making for End-Of-Life Care in Healthcare.

A Concise Overview of Safety Aspects in Human-Robot Interaction

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