Clinical Scorecard: A Scoping Review of Multimodal Artificial Intelligence for Diabetes Care
At a Glance
Category
Detail
Condition
Clinical topic addressed by the source article: A Scoping Review of Multimodal Artificial Intelligence for Diabetes Care
Key Mechanisms
Mechanisms or clinical associations described in the source article.
Target Population
Population described in the source article.
Care Setting
The review examines multimodal AI applications in diabetes complications screening and personalized disease management, including methods, performance evidence, limitations, and implementation barriers.
Key Highlights
The review’s objectives include mapping multimodal AI applications across diabetes complications screening and personalized disease management.
It examines encoder architectures, fusion strategies, and explainability.
It aims to assess evidence for multimodal performance superiority over unimodal baselines.
The authors describe fragmented literature, inconsistent or insufficiently defined terminology, and increasingly varied data-encoding and fusion approaches.
The review protocol was registered with OSF, and the review was conducted in accordance with PRISMA-ScR.
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Risks
The introduction states that consolidated evidence remains limited on the incremental predictive performance of multimodal AI relative to unimodal baselines.
The authors state that critical consideration of model interpretability is lacking and describe interpretability as a prerequisite for clinical trust.
The authors report inconsistent or insufficiently defined terminology for multimodality and increasingly varied data-encoding and fusion approaches.
Patient & Prescribing Data
The review concerns studies involving diabetes mellitus, diabetes complications, or prediabetes, as reflected in its stated search domains; the supplied excerpt does not report specific patient-level prescribing data.
The supplied excerpt describes review objectives and methods but does not provide medication or treatment recommendations.
Clinical Best Practices
Eligibility was determined using prespecified criteria based on the Population, Concept, and Context framework.
Two reviewers independently screened titles and abstracts; disagreements were discussed and, when needed, adjudicated by a senior reviewer.
