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Population ageing will increase spending on health care and long-term care. According to the projections in the 2018 Ageing Report, spending on health care and long-term care as a percentage of gross domestic product (GDP) is projected to rise by an average of 0.7 and 1.3 percentage points respectively over the period 2013-60. As life expectancy increases, the individual, social and financial burden of assisting these disabled patients will particularly grow in developed countries. Therefore, we must rethink the approach to ageing and neurodegenerative diseases according to current needs and new challenges. We need a personalized approach to the single subject: as shown in other fields, such as oncology, this approach may lead to an improved efficacy in treatments and also in significant economic outcome. To this aim Information and Communication Technology (ICT) and Artificial Intelligence (AI) are two major innovative tools which may support healthy ageing and precision medicine in the elderly, reducing the economic costs of care.

Low birth rate and the long life expectancy represent an explosive mixture, resulting in the rapid aging of population. The costs of healthcare in the grey society are increasing dramatically, and soon there will be not enough resources and people for care. This context requires conceptually new elderly care solutions progressively reducing the percentages of the human-based care. Research on robot-based solutions for elderly care and active ageing aims to answer these needs. From a general perspective, robotics has the power to completely reshape the landscape of healthcare both in its structure and its operation. In fact, the long-term sustainability of healthcare systems could be addressed by automation powered by digital health technologies, such as artificial intelligence, 3D-printing or robotics. The latter could take over monotonous work from healthcare workers, which would allow them to focus more on patients and to have lesser workload. Robots might be used in elder care with several different aims. (i) Robots may act as caregivers, i.e. assist the elderly, (ii) they can provide remainders and instructions for activities of daily life and safety, and/or assist their carers in daily tasks; (iii) they can help monitor their behaviour and health; and (iv) provide companionship, including entertainment and hobbies, reminiscence and social contact. The use of Robots with human subjects/patients raise several sensitive questions. First of all, robots may represent information hubs, and can collect an incredible amount of data about the subjects and their environment. In fact, they record habits such as sleeping, exercising, third persons entering in the house, appointments. Communications may be continuously recorded. Moreover, by connecting with medical devices, they can store medical data. On one hand, this represents a very powerful tool to collect information about the single subject (precision medicine), about disease (thus eventually finding new signs and symptoms through artificial intelligence by machine learning and deep learning) and about his/her habitat. On the other, this powerful instrument may represent a dramatic treat to the privacy of the subjects and their caregivers. Therefore, robotics represents an ethically sensitive field. Care robotics bear the risk of reducing human contact, of increasing the objectification and loss of control of the elderly, of losing the privacy and personal freedom of the individual (especially when robots may perform restrictive interventions). Moreover, the use of robots in elderly care may raise in the risk of confusing between reality and appearance, with a potential risk of deception and infantilization of the elder.

Artificial intelligence (AI) deep learning protocols offer solutions to complex data processing and analysis. Increasingly these solutions are being applied in the healthcare field, most commonly in processing complex medical imaging data used for diagnosis. Current models apply AI to screening populations of patients for markers of disease and report detection accuracy rates exceeding those of human data screening. In this paper, we explore an alternate model for AI deployment, that of monitoring and analysing an individual’s level of function over time. In adopting this approach, we propose that AI may provide highly accurate and reliable detection of preclinical disease states associated with aging-related neurodegenerative diseases. One of the key challenges facing clinical detection of preclinical phases of diseases such as dementia is the high degree of inter-individual variability in aging-related changes to cognitive function. AI based monitoring of an individual over time offers the potential for the early detection of change in function for the individual, rather than relying on comparing the individual’s performance to population norms. We explore an approach to developing AI platforms for individual monitoring and preclinical disease detection and examine the potential benefits to the stakeholders in this technological development.

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Copyright of Orientamenti Pedagogici is the property of Pontificio Ateneo Salesiano (Facoltà di Scienze dell'Educazione dell'Università Pontificia Salesian) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)