Note to readers: To solicit this conclusion from ChatGPT, the guest editors described the issue themes, sent it the final articles and asked for the article ideas and themes to be consolidated in a short conclusion. The below is that result, which was also run through one editing cycle by ChatGPT.
As we close this issue of AI and Aging: On Ethics, Health, and Innovation, we reflect not merely on what artificial intelligence is doing for older adults, but what it must do with them, through them, and because of them. This collection outlines both the promise and the pitfalls of AI as it intersects with aging—spanning the personal and political, the medical and ethical, the technological, and deeply human.
The issue opens with a primer by Angad Sandhu, Vishesh Gupta, and Faizan Wajid, who trace AI’s development from its neural foundations to the current era of large language models. Their piece offers a foundational context, grounding readers in the terminology and technological evolution that informs the rest of this collection.
A common thread across these contributions is AI’s transformative potential in aging-related healthcare. From diagnostic algorithms capable of detecting neurodegenerative diseases in their preclinical stages to intelligent monitoring systems in homes and long-term care facilities, the scope of innovation is vast. Yet the message is clear: technology alone is not enough. As Wajid reminds us, “technology must be paired with training, trust, and thoughtful integration” to be truly meaningful in dementia care and beyond. Joe Velderman’s caution is equally urgent: in our rush toward digital agents and predictive analytics, we must not allow human connection to become optional.
The dangers of exclusion—digital, cultural, and structural—are also highlighted. Xinchen Yang’s poignant account of Evelyn, an older woman locked out of essential services due to inaccessible app design, exemplifies “digital ageism.” Age is the only identity category we will all eventually share, yet our AI systems are rarely designed with older users in mind. This is not a technical failure; it is a social one.
‘Authors challenge us to reconceptualize older adults not as passive recipients of care but as active co-creators of technology and policy.’
Indeed, the specter of bias and ageism in AI runs throughout the journal. Dan Andersen, Vishesh Gupta, Nikhil Nirhale, and Faizan Wajid reveal how even generative image models tend to stereotype older adults as frail, white, or dependent. Dorothy Siemon and Karen Lyons draw attention to algorithmic exclusion in hiring, fraud detection, and clinical decision-making—domains where the stakes are particularly high. The data used to train AI systems often exclude older adults, and these gaps propagate silently but powerfully through the models themselves.
And yet, there is cause for optimism. Articles in this issue propose concrete steps forward: co-design with older adults, improved data governance, more representative training datasets, ethical audits, and new forms of AI literacy. Michael Ash’s vision of AI-powered integrative medicine illustrates how Large Language Models and diagnostic tools cannot only extend life but enrich it. Raymond Jetson’s work on AI and community-building among Black elders shows how data tools can strengthen—not sever—cultural ties and civic engagement.
Several authors challenge us to reconceptualize older adults not as passive recipients of care but as active co-creators of technology and policy. Mary Jo Deering’s exploration of virtual villages presents one such model, where grassroots organizations leverage AI to manage operations, tailor services, and measure impact. If democratized, AI could make aging in place not just a preference, but a viable, well-resourced, and respected model of care.
Ravi Hubbly, Wajid, and Andersen broaden the vision even further—positioning AI not merely as a diagnostic tool or scheduling aid, but as a driver of compassionate, strength-based aging. Their work proposes a hybrid model of care in which AI augments human intuition rather than replacing it. They argue that AI should not only flag deficits but also highlight resilience, support independence, and uphold dignity. Their vision includes explainable models, “geriatrician-in-your-pocket” tools, and person-centered data systems that respect lived experience rather than reducing it to risk scores.
What Comes Next
The future of AI in aging is still unwritten—but it is not unshaped. The field is rapidly moving toward systems that are both multimodal and hyper-personalized. In healthcare, this means integrating diverse datasets—imaging, genomics, voice patterns, environmental sensors, and real-time biometrics—to form holistic views of aging individuals (Ash, Wajid). As Ash and Wajid demonstrate, AI systems are increasingly capable of detecting early signs of neurodegeneration or chronic disease through speech, sleep patterns, or wearable data. But with greater complexity comes a greater need for clarity. Explainable AI and transparency standards are essential to ensure that such insights are actionable and trustworthy in clinical settings (Hubbly, Wajid, Andersen).
This technological progress must be guided by a robust ethical framework. Multiple contributors stress the need to embed fairness, accountability, and bias audits into the design of elder care technologies (Siemon, Yang). While regulatory efforts are emerging at state and federal levels, they remain inconsistent and fragmented (Siemon). The call is clear: AI policy must center the lived experiences of older adults and protect them from both neglect and overreach.
‘As automation advances, the role of
the human caregiver must not be diminished.’
Cultural and demographic inclusion will shape the next frontier. As Jetson and Yang argue, the needs of Black elders and digitally excluded or historically marginalized groups cannot be met with one-size-fits-all solutions. Aging While Black, for instance, offers a model of culturally embedded AI tools that foster community cohesion and elevate elder leadership. Yang advocates for co-design approaches that engage older adults directly in the technology development process—ensuring not just usability but dignity and affirmation. For both, inclusive AI is not only more ethical—it is more effective.
As automation advances, the role of the human caregiver must not be diminished. Velderman and Mara Cai and Ashok Agrawala push back against the false binary of replacement versus preservation. AI should relieve caregivers of administrative burdens—but its greatest value lies in creating more space for presence, compassion, and relationships. Technology should augment, not erode, the relational core of elder care.
Equally vital is the question of consent, data ownership, and digital rights. As long-term care centers and smart homes become saturated with data-gathering technologies, older adults must retain visibility into—and control over—how their data is used (Velderman, Siemon). Transparent data-sharing and age-inclusive privacy protections will be critical to building trust, especially among populations who may already feel surveilled or disempowered.
Finally, this work must not occur in silos. The most compelling visions in this issue—from Ash’s integrative medicine to Deering’s village-based empowerment to Hubbly’s strength-based care—emphasize the need for cross-sector collaboration. The future of AI and aging requires alignment among clinicians, technologists, ethicists, policymakers, and—above all—older adults themselves. Aging is not a niche concern; it is a universal one.
AI may be the tool, but the future remains human. As we design algorithms, let us not forget who they are for. As we build systems, let us not displace the wisdom of lived experience. And above all, let us remember: the dignity with which a society supports its older adults reflects not only its ethics—but its imagination.
The age of intelligent care has arrived. May we meet it with wisdom.
Photo credit: Miriam Doerr Martin Frommherz
