This current issue of Healthovation Magazine explores the theme of ‘Ageing in the Digital Age: Innovations in Care, Technology, and Well-Being’ which I consider a very pertinent theme given the opportunities and challenges facing the industry.
In recent years, the convergence of artificial intelligence (AI), robotics, and digital health technologies has revolutionized how healthcare is conceptualized and delivered. This transformation is particularly impactful in the realm of elderly healthcare, where the need for innovative, efficient, and humane solutions is increasingly urgent. As Australia’s National Science Agency (CSIRO) outlines in its 2024 “AI Trends for Healthcare” report, a digital health revolution is underway. It is characterized by advances in interoperability, cloud computing, personalized apps, and predictive analytics—all of which are underpinned by AI and machine learning. This editorial examines the transformative potential of AI and robotics in elderly healthcare, emphasizing independence, precision medicine, care coordination, and ethical considerations.
AI in Healthcare: A Paradigm Shift According to Australia’s National Science Agency
CSIRO highlights a critical inflection point: the fusion of rising data availability, cloud-based computing power, and machine learning algorithms is enabling AI to transcend pilot studies and become embedded in everyday healthcare practices. Unlike other sectors, healthcare’s reliance on AI is subject to tight regulatory scrutiny because AI often functions as a clinical decision-maker, termed “Software as a Medical Device (SaMD).” As Australia prepares its infrastructure and regulatory frameworks, key trends have emerged:
- Interoperability: Ensuring seamless data sharing between providers enhances continuity of care.
- Personalization: AI enables tailored treatment plans based on real-time and historical data.
- Data-driven Insights: Predictive models support preventive care by identifying risks before acute episodes.
These foundational AI strategies are now expanding into specialized areas such as geriatric care, where chronic diseases, multimorbidity, and cognitive decline demand continuous monitoring and personalized support.
Promoting Independence and Quality of Life in Elderly Care
Aging populations worldwide, including in Australia, face the dual challenges of increased healthcare demand and workforce shortages in geriatric medicine. AI-powered tools offer scalable solutions to support autonomy and quality of life for older adults. According to Padhan et al. (2023), intelligent systems now assist with:
- Daily Activities: Voice-activated assistants and ambient home sensors aid in medication adherence, fall detection, and mobility.
- Wearable Technologies: Devices monitoring vital signs help detect early health deterioration, enabling proactive interventions.
- Home Safety: AI algorithms assess deviations in behaviour patterns and trigger emergency alerts without human input.
Social robotics further enhance mental well-being. Robots like PARO (Therapeutic Robot) and CuDDler (capable of affective social interaction) provide emotional support to individuals with dementia, alleviating loneliness and stimulating cognitive engagement.
Moreover, the Australian Government’s Aged Care Digital Transformation Strategy is guiding aged care providers in acquiring and using technology to support care delivery. The strategy statement emphasises, “Aged care, both residential care and community-based care, is actively looking for solutions to assist in providing enhanced care while also implementing the Australian Government’s Aged Care Digital Transformation Strategy.” It also outlines several key technological platforms such as,
- Assistive Technologies: Development of innovative assistive technologies to support day-to-day functioning for older Australians living at home or in residential care facilities.
- Wearable Medical Devices: Increasing use of wearable devices for health monitoring and clinical vigilance.
- Clinical Decision Support: Algorithms for clinical decision-making and preventative risk management.
- Smart Homes: Integration of smart home technologies to enable independent living for older adults.
- Mobile Health Applications: Apps and clinician platforms to support chronic conditions, such as secondary prevention of stroke.
- Eye Health Diagnosis and Prevention: Research into technologies for eye health monitoring and prevention.
- Falls Prevention: Development of AI-driven tools to prevent falls among older adults.
- Responsible AI Use: Ensuring ethical and responsible implementation of AI in aged care.
Main challenges facing the industry are addressing the lack of rigorous evidence for AI-enabled products, educating users on safe and effective use, and conducting multidisciplinary research to validate AI technologies in aged care. Nevertheless, these trends aim to enhance care quality, improve independence, and address workforce challenges in the aged care sector.
Stratified and Predictive Care through AI
AI is also instrumental in stratifying elderly patients based on risk and providing customized medical responses. Shiwani et al. (2023) identify the following core applications:
- Predictive Diagnostics: Machine learning models analyze patterns in imaging and speech to identify dementia, delirium, or depression early.
- Decision Support Systems (DSS): Algorithms embedded in Electronic Health Records (EHRs) assist clinicians in selecting optimal treatments based on individual profiles.
- Personalized Nutrition and Lifestyle Guidance: AI tailors dietary and exercise recommendations to manage chronic diseases like diabetes and hypertension.
The promise of stratified care is that it not only extends life expectancy but also improves functional outcomes and quality of life. However, it must be balanced with adequate validation, as many current AI models lack generalizability across diverse older populations.
Revolutionizing Geriatric Rehabilitation and Assistance through Robotics
Robotics complements AI by offering physical support and rehabilitation. Padhan et al. (2023) underscore several key developments:
- Assistive Robots: Robots equipped with actuators (a device that causes a machine or other device to operate )aid in mobility, toileting, and meal preparation.
- Rehabilitative Exoskeletons: Wearable robotics enhance strength and mobility, particularly after stroke or orthopaedic surgery.
- Social and Therapeutic Interaction: Robots that interact via speech or touch have been shown to uplift mood and reduce agitation in patients with cognitive impairments.
In long-term care facilities, robotic technology also reduces the workload of nursing staff by automating routine tasks, thereby freeing caregivers to focus on human-centered care.
System-Level Coordination and Smart Infrastructure
AI not only improves individual patient outcomes but also strengthens healthcare systems. Applications include:
- Telemedicine Platforms: AI-driven virtual consultations enhance access in rural and underserved areas.
- Clinical Information Extraction: Natural Language Processing (NLP) tools summarize unstructured Electronic Health Record data for faster clinical decision-making.
- Workflow Optimization: Predictive analytics guide staffing and resource allocation in aged care settings.
The CSIRO’s Australian e-Health Research Centre (AEHRC) has played a leading role by deploying tools like CogStack (retrieves information from both structured and unstructured health data) and MayaMD (Patient engagement Healthcare AI), which streamline communication between multidisciplinary teams and support real-time diagnostics.
Ethical Challenges in AI-Driven Elderly Healthcare
Despite its promise, the use of AI and robotics in elderly care raises profound ethical questions. According to both Shiwani et al. (2023) and Padhan et al. (2023), key concerns include:
- Privacy: Wearables and ambient sensors collect sensitive data, raising concerns about consent and misuse.
- Bias and Equity: AI models often lack representation of the oldest-old and marginalized groups, risking biased decisions.
- Autonomy and Dehumanization: Overreliance on robots may reduce human contact, leading to feelings of isolation and infantilization.
To mitigate these risks, ethical frameworks must include:
- Transparent data governance and audit trails.
- Inclusive design involving older people from diverse backgrounds.
- Regulatory oversight ensuring safety, efficacy, and respect for human dignity.
Designing an Inclusive AI Future for Ageing Societies
In conclusion, as Australia and the world confront the demographic shift towards older populations, AI and robotics offer indispensable tools for expanding the boundaries of healthcare. From predicting falls to managing multimorbidity, and from social interaction to surgical assistance, these technologies can transform elder care. But their success depends not only on technical innovation but also on responsible governance, inclusivity, and trust.
The path forward must prioritize human-centered design, multidisciplinary collaboration, and continuous evaluation. With careful implementation, AI and robotics can enable older adults to live not just longer, but better lives—empowered, connected, and respected.
Editorial by,
Associate Professor (Dr) Chandana Hewege, PhD, PFHEA
Chairman of the HCI Governing Board | A/Prof, Department of Management & Marketing, School of Business, Law & Entrepreneurship, Swinburne University, Australia. | Principal Fellow, Higher Education Academy, UK | Member of the Australian Institute of Company Directors | Certified Carbon Literacy Trainer, The Carbon Literacy Project, UK.