Artificial Intelligence and digital medicine for integrated home care services in Italy: Opportunities and Limits

Home healthcare in the Italian health system has proven to be an essential factor in adequately responding to the health needs of an increasingly aging population. The opportunities offered by digitization and new technologies, such as artificial intelligence (AI) and robotics, are a lever for making home care services more effective and efficient on the one hand, and on the other for improving remote patient monitoring. Telemedicine devices have enormous potential for telemonitoring and telerehabilitation of patients suffering from chronic disabling diseases; in particular, AI systems can now provide very useful managerial and decision-making support in numerous clinical areas. AI combined with digitalization, could also allow for the remote monitoring of patients' health conditions. In this paper authors describe some digital and healthcare tools or system of AI, such as the Connected Care model, the Home Care Premium (HCP) project, The Resilia App and some professional service robotics. In this context, to optimize potential and concrete healthcare improvements, some limits need to be overcome: gaps in health information systems and digital tools at all levels of the Italian National Health Service, the slow dissemination of the computerized medical record, issues of digital literacy, the high cost of devices, the poor protection of data privacy. The danger of over-reliance on such systems should also be examined. Therefore the legal systems of the various countries, including Italy, should indicate clear decision-making paths for the patient

Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

Robotic technology for palliative and supportive care: Strengths, weaknesses, opportunities and threats

Background: Medical robots are increasingly used for a variety of applications in healthcare. Robots have mainly been used to support surgical procedures, and for a variety of assistive uses in dementia and elderly care. To date, there has been limited debate about the potential opportunities and risks of robotics in other areas of palliative, supportive and end-of-life care. Aim: The objective of this article is to examine the possible future impact of medical robotics on palliative, supportive care and end-of-life care. Specifically, we will discuss the strengths, weaknesses, opportunities and threats (SWOT) of this technology. Methods: A SWOT analysis to understand the strengths, weaknesses, opportunities and threats of robotic technology in palliative and supportive care. Results: The opportunities of robotics in palliative, supportive and end-of-life care include a number of assistive, therapeutic, social and educational uses. However, there are a number of technical, societal, economic and ethical factors which need to be considered to ensure meaningful use of this technology in palliative care. Conclusion: Robotics could have a number of potential applications in palliative, supportive and end-of-life care. Future work should evaluate the health-related, economic, societal and ethical implications of using this technology. There is a need for collaborative research to establish use-cases and inform policy, to ensure the appropriate use (or non-use) of robots for people with serious illness

\u3ci\u3eMedicine Meets Virtual Reality 17\u3c/i\u3e

Chapter, A Virtual Reality Training Program for Improvement of Robotic Surgical Skills, co-authored by Mukul Mukherjee and Nicholas Stergiou, UNO faculty members. Chapter, Consistency of Performance of Robot-Assisted Surgical Tasks in Virtual Reality, co-authored by Mukul Mukherjee and Nicholas Stergiou, UNO faculty members. The 17th annual Medicine Meets Virtual Reality (MMVR17) was held January 19-22, 2009, in Long Beach, CA, USA. The conference is well established as a forum for emerging data-centered technologies for medical care and education. Each year, it brings together an international community of computer scientists and engineers, physicians and surgeons, medical educators and students, military medicine specialists and biomedical futurists. MMVR emphasizes inter-disciplinary collaboration in the development of more efficient and effective physician training and patient care. The MMVR17 proceedings collect 108 papers by conference lecture and poster presenters. These papers cover recent developments in biomedical simulation and modeling, visualization and data fusion, haptics, robotics, sensors and other related information-based technologies. Key applications include medical education and surgical training, clinical diagnosis and therapy, physical rehabilitation, psychological assessment, telemedicine and more. From initial vision and prototypes, through assessment and validation, to clinical and academic utilization and commercialization - MMVR explores the state-of-the-art and looks toward healthcare’s future. The proceedings volume will interest physicians, surgeons and other medical professionals interested in emerging and future tools for diagnosis and therapy; educators responsible for training the next generation of doctors and scientists; IT and medical device engineers creating state-of-the-art and next-generation simulation, imaging, robotics and communication systems; data technologists creating systems for gathering, processing and distributing medical intelligence; military medicine specialists addressing the challenges of warfare and defense health needs; and biomedical futurists and investors who want to understand where the field is headed.https://digitalcommons.unomaha.edu/facultybooks/1233/thumbnail.jp

Robot Autonomy for Surgery

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots

Can We Agree on What Robots Should be Allowed to Do? An Exercise in Rule Selection for Ethical Care Robots

Future Care Robots (CRs) should be able to balance a patient’s, often conflicting, rights without ongoing supervision. Many of the trade-offs faced by such a robot will require a degree of moral judgment. Some progress has been made on methods to guarantee robots comply with a predefined set of ethical rules. In contrast, methods for selecting these rules are lacking. Approaches departing from existing philosophical frameworks, often do not result in implementable robotic control rules. Machine learning approaches are sensitive to biases in the training data and suffer from opacity. Here, we propose an alternative, empirical, survey-based approach to rule selection. We suggest this approach has several advantages, including transparency and legitimacy. The major challenge for this approach, however, is that a workable solution, or social compromise, has to be found: it must be possible to obtain a consistent and agreed-upon set of rules to govern robotic behavior. In this article, we present an exercise in rule selection for a hypothetical CR to assess the feasibility of our approach. We assume the role of robot developers using a survey to evaluate which robot behavior potential users deem appropriate in a practically relevant setting, i.e., patient non-compliance. We evaluate whether it is possible to find such behaviors through a consensus. Assessing a set of potential robot behaviors, we surveyed the acceptability of robot actions that potentially violate a patient’s autonomy or privacy. Our data support the empirical approach as a promising and cost-effective way to query ethical intuitions, allowing us to select behavior for the hypothetical CR