Alzheimers Dement May 2023 PMID 36735865

Interpretable machine learning for dementia: A systematic review.

Sophie A Martin, Florence J Townend, Frederik Barkhof, James H Cole

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Mentions

Peak: 25 in Feb 2023

across 2 sources since Feb 2023

Extended reach

40,148

potential audience

Total likes

Video views

305

Mentions per month

Feb 2023 – Feb 2024

Where it's discussed

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AI synopsis

What people are saying

The overall discussions reveal a positive reception, with many posts celebrating the publication, such as 'My first paper got published!' and 'Great to see this work,' showcasing enthusiasm. The tone is supportive but not highly critical or deeply analytical, indicating moderate engagement with the research's clinical implications. Words like 'out now' and mentions of AI and interpretability underscore the community’s interest in advancing dementia diagnosis, but the focus remains more on recognition than extensive debate.

AgreementModerate agreement

Most posts express support or positive acknowledgment of the publication, with phrases like 'great to see this work' and 'out now in A&D,' indicating endorsement and recognition of its significance.

InterestHigh interest

The enthusiasm around publication announcements and references to methods and potential improvements suggest strong curiosity and relevance within the community.

EngagementModerate engagement

Comments mention methodological aspects and clinical implications, but mostly focus on sharing the publication rather than deep critique or discussion.

ImpactModerate impact

While the posts highlight the importance of interpretability in dementia diagnosis, they generally suggest moderate perceived impact, emphasizing future application rather than immediate clinical change.

Abstract in brief

The review highlights that while machine learning models for dementia detection show promising results, many studies lack consistent validation and rely on common datasets, limiting their clinical applicability.
Building models that are inherently interpretable or using methods to explain complex models are both important, but more collaboration with clinicians is needed to ensure explanations truly aid in understanding dementia.
Future research should focus on patient-specific explanations and better validation of interpretability methods to improve the practical use of machine learning in diagnosing dementia in real-world clinical settings.

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