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ARXIV:2605.03103 · MEDICAL AI · SUBMITTED 06 MAY · 20:25 UTC · FRESHNESS STALE
ARXIV:2605.03103MEDICAL AISUBMITTED 06 MAY · 20:25 UTCFRESHNESS STALEYingyun Li · Yu Wang · Haiyang Qian · arXiv
Introducing MedStruct-S, a benchmark for evaluating information extraction from noisy clinical reports, enabling robust AI for patient history reconstruction.
Opportunity summary
Pain Introducing MedStruct-S, a benchmark for evaluating information extraction from noisy clinical reports, enabling robust AI for patient history reconstruction.
Evidence 0 refs | 3 sources | 50% coverage
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Introducing MedStruct-S, a benchmark for evaluating information extraction from noisy clinical reports, enabling robust AI for patient history reconstruction. In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned question…
Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical histories. In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned question answering (QA),…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results show that encoder-only models achieve the best performance for non-null-value key-conditioned QA despite being substantially smaller than decoder-only models. Code availability is…
Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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Introducing MedStruct-S, a benchmark for evaluating information extraction from noisy clinical reports, enabling robust AI for patient history reconstruction.
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10.48550/arXiv.2605.03103Introducing MedStruct-S, a benchmark for evaluating information extraction from noisy clinical reports, enabling robust AI for patient history reconstruction.
Abstract
Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical histories. In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned question answering (QA), and (iii) end-to-end key-value pair extraction. However, existing evaluations often under-model two factors: heterogeneous and incompletely known key representations, and OCR-induced noise. This makes it difficult to assess model robustness in real-world settings. We present MedStruct-S, a benchmark specifically designed to evaluate these tasks under unknown keys and OCR noise. MedStruct-S contains 3,582 annotated real-world clinical report pages. Using MedStruct-S, we benchmark two representative paradigms: encoder-only sequence labeling with post-processing and decoder-only structured generation, covering four encoder-only and five decoder-only models spanning 0.11B to 103B parameters. Our results show that encoder-only models achieve the best performance for non-null-value key-conditioned QA despite being substantially smaller than decoder-only models. When comparing models of similar order of magnitude, encoder-only models still perform better overall. Without controlling for model scale, fine-tuned decoder-only models deliver the strongest overall results. These findings show that the benchmark provides a reliable and practical basis for selecting and comparing models across different semi-structured IE settings.
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PROBLEM
Introducing MedStruct-S, a benchmark for evaluating information extraction from noisy clinical reports, enabling robust AI for patient history reconstruction. In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned questio...
METHOD
Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical histories. In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned ques...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results show that encoder-only models achieve the best performance for non-null-value key-conditioned QA despite being substantially smaller than decoder-only models. Code availability is flagged in t...
WHY NOW
Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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Introducing MedStruct-S, a benchmark for evaluating information extraction from noisy clinical reports, enabling robust AI for patient history reconstruction. In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned question answering (QA), and (iii) end-to-end key-value pair extraction.
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partial
Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical histories. In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned question answering (QA), and (iii) end-to-end key-value pair extraction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results show that encoder-only models achieve the best performance for non-null-value key-conditioned QA despite being substantially smaller than decoder-only models. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Introducing MedStruct-S, a benchmark for evaluating information extraction from noisy clinical reports, enabling robust AI for patient history reconstruction.
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