Summary
This scoping review synthesises emerging evidence on artificial intelligence-assisted dietary assessment tools and their potential applications in clinical nutrition practice. The authors examine how machine learning approaches might enhance traditional dietary assessment methods whilst mapping evidence gaps and implementation challenges. The work contributes to understanding how digital innovations could support more efficient and personalised nutrition care delivery, though significant practical adoption barriers in healthcare settings persist.
Regional applicability
The findings are relevant to UK nutrition practice and NHS implementation contexts, particularly in supporting personalised nutrition interventions and digital health initiatives. However, applicability depends on addressing clinical workflow integration, data governance, and validation in UK population cohorts.
Key measures
Scope and characteristics of AI-assisted dietary assessment tools; evidence of effectiveness and clinical utility; implementation barriers and evidence gaps in nutrition care delivery
Outcomes reported
The review identified and synthesised evidence on machine learning and algorithmic applications in dietary assessment, examining their potential to enhance traditional assessment methods in clinical nutrition practice. The study evaluated implementation feasibility, effectiveness, and barriers to adoption in healthcare settings.
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