Using AI for Dementia Prevention: Insights from the Athens Alzheimer’s Association Workshop on Personalized Strategies and Technology Integration
The workshop, held at the Athens Alzheimer’s Association, brought together participants concerned about cognitive health and dementia prevention, divided into two groups: Group A (individuals at risk or concerned about dementia and cognitive decline) and Group B (healthcare professionals working with dementia patients). The primary goals were to explore how technology, particularly Artificial Intelligence and machine learning techniques, could support dementia prevention, and to identify unmet needs in dementia care and education, with a special focus on the potential impact of personalized preventive strategies and the role of technology in identifying at-risk individuals and providing risk reduction guidelines.
The key insights gathered, for each participants’ category, from the workshop were:
Group A:
- Personalized prevention as a motivator: Personalized strategies were seen as more effective in motivating lifestyle changes, offering actionable steps based on individual risk factors.
- Preferred Application Types:
- Educative Tools: Providing scientific data on cognition and dementia risk.
- Practical Tips and Resources: Offering resources like health services and apps to assist lifestyle adjustments.
- Willingness to Share Information: Most participants in Group A were open to share comprehensive information if it would contribute to a more accurate risk assessment. However, some were cautious, preferring to share only basic demographic details, due to concerns about data privacy and misuse.
- Risk Assessment and Preventive Strategies: Group A participants were primarily interested in learning about their personal risk for dementia and receiving lifestyle-based preventive strategies. This was particularly important to individuals with a family history of dementia.
Group B:
- Technology in Risk Identification: Group B participants acknowledged the utility of technology in identifying at-risk individuals. However, they stressed that any technological assessment should be reviewed by healthcare professionals before final decisions are made.
- Support for Risk Reduction Guidelines: Healthcare professionals showed an appreciation on the potential of such apps in providing risk reduction guidelines, but emphasized the importance of professional oversight in the final decision-making process.
- Simplicity and Relevance: Group B participants preferred the inclusion of only essential, well-researched information in such applications. They emphasized that the apps should be user-friendly, requiring only simple and relevant data.
- Guidelines for Patients: Healthcare professionals in Group B valued the idea of receiving concise, actionable guidelines from such apps, which they could then pass on to patients. They highlighted the need for practical advice that could be easily integrated into their clinical practice, given time constraints.
- Skepticism and Potential: While skeptical about the direct use of these technologies in developing therapies, participants recognized their potential in identifying at-risk individuals who could be recruited for clinical trials. They saw the technology as a tool for targeting specific populations rather than as a direct means of therapy development.
These findings underscore the importance of creating dementia-prevention tools that are personalized, privacy-conscious, professionally supervised, and rich in educational content to serve both patients and healthcare professionals effectively.
**Article written by National and Kapodistrian University of Athens, a key partner in the COMFORTage project.
