The datasets used are relatively small and may not fully represent real-world clinical diversity.
Research gap analysis derived from 3 computer_science papers in our local library.
The gap
The datasets used are relatively small and may not fully represent real-world clinical diversity.
Consensus across the literature
Clustered from 3 gap mentions across 3 papers via embedding cosine ≥ 0.62.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- AI-Based Blood Groups Prediction and Classification Through Image Processing Using CNN (2026) · doi
The dataset used for training the blood group classification model lacks diversity across imaging equipment, lighting conditions, and patient populations; expansion to include images from multiple imaging modalities and varying environmental conditions is needed to improve generalizability in real-world scenarios.
Keywords: blood sample images dataset diversity imaging equipment lighting conditions populations - Explainable artificial intelligence for cross domain evaluation of predictive models in multi-disease diagnosis (2026) · doi
The datasets used are relatively small and may not fully represent real-world clinical diversity.
Keywords: datasets used relatively small fully represent real world clinical diversity - Web-Based Platform for Multi-Modal Medical Image Analysis Using X-Rays, MRI, and CT Data (2026) · doi
The dataset composition, size, and diversity are not detailed; it is unclear how representative the dataset is of real clinical populations.
Keywords: dataset composition size diversity detailed unclear representative real clinical populations
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