Medicine & AI

Abstract

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

Abstract

IMPORTANCE Artificial intelligence (AI) and novel technologies, such as remote sensors, robotics, and decision support algorithms, offer the potential for improving the health and well-being of older adults, but the priorities of key partners across the technology innovation continuum are not well understood.

OBJECTIVE

To examine the priorities and suggested applications for AI and novel technologies for older adults among key partners.

DESIGN, SETTING, AND PARTICIPANTS

This qualitative study comprised individual interviews using grounded theory conducted from May 24, 2023, to January 24, 2024. Recruitment occurred via referrals through the Johns Hopkins Artificial Intelligence and Technology Collaboratory for Aging Research. Participants included adults aged 60 years or older or their caregivers, clinicians, leaders in health systems or insurance plans (ie, payers), investors, and technology developers.

MAIN OUTCOMES AND MEASURES

To assess priority areas, older adults, caregivers, clinicians, and payers were asked about the most important challenges faced by older adults and their caregivers, and investors and technology developers were asked about the most important opportunities associated with older adults and technology. All participants were asked for suggestions regarding AI and technology applications. Payers, investors, and technology developers were asked about end user engagement, and all groups except technology developers were asked about suggestions for technology development. Interviews were analyzed using qualitative thematic analysis. Distinct priority areas were identified, and the frequency and type of priority areas were compared by participant groups to assess the extent of overlap in priorities across groups.

RESULTS

Participants included 15 older adults or caregivers (mean age, 71.3 years [range, 65-93 years]; 4 men [26.7%]), 15 clinicians (mean age, 50.3 years [range, 33-69 years]; 8 men [53.3%]), 8 payers (mean age, 51.6 years [range, 36-65 years]; 5 men [62.5%]), 5 investors (mean age, 42.4 years [range, 31-56 years]; 5 men [100%]), and 6 technology developers (mean age, 42.0 years [range, 27-62 years]; 6 men [100%]). There were different priorities across key partners, with the most overlap between older adults or caregivers and clinicians and the least overlap between older adults or caregivers and investors and technology developers. Participants suggested novel applications, such as using reminders for motivating self-care or social engagement. There were few to no suggestions that addressed activities of daily living, which was the most frequently reported priority for older adults or caregivers. Although all participants agreed on the importance of engaging end users, engagement challenges included regulatory barriers and stronger influence of payers relative to other end users.

CONCLUSIONS AND RELEVANCE

This qualitative interview study found important differences in priorities for AI and novel technologies for older adults across key partners. Public health, regulatory, and advocacy strategies are needed to raise awareness about these priorities, foster engagement, and align incentives to effectively use AI to improve the health of older adults.