Author

Sayantan Kumar

3 papers indexed here

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#artificial intelligence May 2026

Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment

A retrieval-augmented multimodal alignment framework that bridges this gap to improve the temporal precision of absolute clinical timelines extracted from text and shows that aligning these modalities can produce a more temporally faithful and clinically informative reconstruction of patient trajectories than either so...

Sayantan Kumar, Shahriar Noroozizadeh, Juyong Kim et al. · arXiv.org · 3 citations

#artificial intelligence Preprint Sep 2026

Hindsight Bias in Clinical Temporal Reasoning: How Future Data Exposure Affects Large Language Model Judgment

A paired benchmark for measuring outcome-conditioned shifts consistent with hindsight bias in clinical temporal reasoning is introduced and accuracy (Acc), hindsight trap rate (HTR), answer instability rate (AIR), and hindsight bias rate (HBR) are evaluated.

Misaki Matsuura, Sayantan Kumar, Ojas Kadam et al. · 1 citation

#artificial intelligence Review Sep 2026

Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication

In developing the UID and judge pipeline, this work is able to demonstrate 43\% increased event recovery, a framework competitive with clinician annotations, and a system with occurrence-level provenance for both reconstruction and evaluation.

Sayantan Kumar, Nicolas Grimaldi, Jack A. Cummins et al. · 0 citations