AI in early dementia diagnosis: Beyond technological solutionism in clinical practice

Artificial intelligence (AI) holds considerable promise for improving early dementia diagnosis, but its translation into clinical practice faces significant challenges that warrant careful consideration. The field’s emphasis on “technological solutionism” may lead to developing models with limited demonstrated real-world value. We identify four foundational concerns constraining clinical translation: selection bias and data leakage from using highly specialized research cohorts unrepresentative of primary care populations; inherent uncertainty in diagnostic “ground truth” labels, which places constraints on meaningful model performance evaluation; circular logic, where models may become “complexity launderers” that repackage existing clinical data without adding substantial new insights; and clinical-ethical mismatch, where tools designed for specialist settings are potentially misapplied to primary care, creating an “ethical burden of prediction” for conditions with limited therapeutic options.To support responsible innovation, we propose a paradigm shift from narrow accuracy focus to comprehensive value evaluation. We advocate for a mandatory four-axis framework assessing all new diagnostic tools: Analytical Validity (accounting for label uncertainty), Clinical Validity (in relevant populations), Clinical Utility (on outcomes meaningful to patients and families), and Ethical, Legal, and Social Viability (including integrated support pathways). Adopting this rigorous, multi-dimensional standard is essential to guide AI from promising technology toward mature clinical science that responsibly serves patients and families.