ArticleBMC medical informatics and decision making2026
Use of explainable AI (xAI) in dementia detection and prognosis: a scoping review.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Conversational AI in Cognitive and Social Training for People with Dementia: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dementia poses a significant global health challenge for both clinicians and patients, impacting millions of individuals worldwide, yet its early diagnosis remains underexplored. The current technology-driven dementia care solutions are revolutionising this landscape with state-of-the-art methodologies (such as Artificial Intelligence (AI)); however, due to its black-box nature, there is a need for Explainable AI (xAI) to help build trust and confidence among end-users, making it more suitable for real-world healthcare applications. This scoping review aims to provide a comprehensive overview of the current xAI usage in this field by synthesising data from studies published since 2014. Through a structured literature extraction process following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines, a total of 415 scientific papers were screened, resulting in 70 eligible articles for an in-depth analysis regarding dementia detection and prognosis using xAI techniques. Most studies relied on public datasets (e.g. ADNI) without clinical validation. A detailed thematic analysis presents the findings of this review, which identifies the most widely used tools, approaches, types of data, and the key limitations/challenges in implementing xAI for dementia detection and prognosis used in the latest research. These findings provide valuable insights and direction for future research in this field by highlighting the underutilization of multimodal data integration, persistent inconsistencies in feature importance rankings across methods, and the imprecision of visual explanations.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.