ARXIV:2602.21374 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

arXiv

Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models. This study evaluates a two-step pipeline combining Aya-expanse-8B as a Persian-to-English translation model with five open-source small…

METHOD

Full abstract

Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP). This study evaluates a two-step pipeline combining Aya-expanse-8B as a Persian-to-English translation model with five open-source small language models (SLMs) -- Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Llama-3.2-3B-Instruct, Qwen2.5-1.5B-Instruct, and Gemma-3-1B-it -- for binary extraction of 13 clinical features from 1,221 anonymized Persian transcripts collected at a cancer palliative care call center. Using a few-shot prompting strategy without fine-tuning, models were assessed on macro-averaged F1-score, Matthews Correlation Coefficient (MCC), sensitivity, and specificity to account for class imbalance. Qwen2.5-7B-Instruct achieved the highest overall performance (median macro-F1: 0.899; MCC: 0.797), while Gemma-3-1B-it showed the weakest results. Larger models (7B--8B parameters) consistently outperformed smaller counterparts in sensitivity and MCC. A bilingual analysis of Aya-expanse-8B revealed that translating Persian transcripts to English improved sensitivity, reduced missing outputs, and boosted metrics robust to class imbalance, though at the cost of slightly lower specificity and precision. Feature-level results showed reliable extraction of physiological symptoms across most models, whereas psychological complaints, administrative requests, and complex somatic features remained challenging. These findings establish a practical, privacy-preserving blueprint for deploying open-source SLMs in multilingual clinical NLP settings with limited infrastructure and annotation resources, and highlight the importance of jointly optimizing model scale and input language strategy for sensitive healthcare applications.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Qwen2.5-7B-Instruct achieved the highest overall performance (median macro-F1: 0.899; MCC: 0.797), while Gemma-3-1B-it showed the weakest results.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainDevelop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.21374 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

arXiv

Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainDevelop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.21374

Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback27 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models. This study evaluates a two-step pipeline combining Aya-expanse-8B as a Persian-to-English translation model with five open-source small language models (SLMs) -- Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Llama-3.2-3B-Instruct, Qwen2.5-1.5B-Instruct, and Gemma-3-1B-it -- for binary extraction of 13 clinical features from 1,221 anonymized Persian transcripts collected at a cancer palliative care call center.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP). This study evaluates a two-step pipeline combining Aya-expanse-8B as a Persian-to-English translation model with five open-source small language models (SLMs) -- Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Llama-3.2-3B-Instruct, Qwen2.5-1.5B-Instruct, and Gemma-3-1B-it -- for binary extraction of 13 clinical features from 1,221 anonymized Persian transcripts collected at a cancer palliative care call center.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Qwen2.5-7B-Instruct achieved the highest overall performance (median macro-F1: 0.899; MCC: 0.797), while Gemma-3-1B-it showed the weakest results.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Medical AI

Competitors

not indexed

Competitive landscape

Develop privacy-preserving tools for extracting clinical information from medical transcripts in low-resource languages using small language models.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.21374 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(27)

OpenAI GPT-5 System Card

2025Aaditya K. Singh, A. Fry et al.

Human level information extraction from clinical reports with finetuned language models

2025Longchao Liu, L. Lian et al.

Enhancing Clinical Named Entity Recognition via Fine-Tuned BERT and Dictionary-Infused Retrieval-Augmented Generation

2025S. Sreenivas, Saqib Chowdhury et al.

Small Language Models are the Future of Agentic AI

2025Peter Belcák, Greg Heinrich et al.

Psychomedical named entity recognition method based on multi-level feature extraction and multi-granularity embedding fusion

2025Zixuan Liu, Guofang Zhang et al.

The Rise of Small Language Models in Healthcare: A Comprehensive Survey

2025Muskan Garg, Shaina Raza et al.

Towards scalable and cross-lingual specialist language models for oncology

2025Morteza Rohanian, Tarun Mehra et al.

Automated Extraction of Key Entities from Non-English Mammography Reports Using Named Entity Recognition with Prompt Engineering

2025Z. Akçalı, Hazal Selvi Çubuk et al.

Fine-Tuning LLMs with Medical Data: Can Safety Be Ensured?

2025Minkyoung Kim, Yunha Kim et al.

Qwen2.5 Technical Report

2024Qwen An Yang, Baosong Yang et al.

A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

2024Fali Wang, Zhiwei Zhang et al.

The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making

2024Mary Summer Starling, Lindsay Kehoe et al.

The Llama 3 Herd of Models

2024Abhimanyu Dubey, Abhinav Jauhri et al.

DeepSeek-V3 Technical Report

2024DeepSeek-AI, A. Liu et al.

Named Entity Recognition in Electronic Health Records: A Methodological Review

2023María C. Durango, E. A. Torres-Silva et al.

Clinical named entity recognition and relation extraction using natural language processing of medical free text: A systematic review

2023David Fraile Navarro, K. Ijaz et al.

Approach to machine learning for extraction of real-world data variables from electronic health records

2023Blythe Adamson, Michael L. Waskom et al.

A rule‐based approach for automatically extracting data from systematic reviews and their updates to model the risk of conclusion change

2020R. Bashir, A. Dunn et al.

Array programming with NumPy

2020Charles R. Harris, K. Millman et al.

Challenges and opportunities for public health made possible by advances in natural language processing.

2020O. Baclic, M. Tunis et al.

Showing 20 of 27 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkEvaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset

5.0

Extension

Builds On ThisPrivacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients

0.0

Builds On ThisWhen Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations

0.0

Builds On ThisSame Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs

4.0

Commercially relevant

Higher ViabilitySelf-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction

7.0

Higher ViabilityDomain-Adapted Small Language Models for Reliable Clinical Triage

7.0

Higher ViabilityAutomating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models

7.0

Higher ViabilityPVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models

8.0

Higher ViabilityGeneralistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages

8.0

Conflicting

Competing ApproachHarnessing Large Language Models for Precision Querying and Retrieval-Augmented Knowledge Extraction in Clinical Data Science

5.0

Related Resources

What advancements are being made in medical AI?(question)
What advancements are being made in medical AI?(question)
Why is evidence-grounded reasoning important in medical AI?(question)
Medical AI – Use Cases(use_case)

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.21374
Route: /paper/small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages
Active tab: read
Artifact: small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages/paper-pack
REST build passport API/api/v1/paper/small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

Canonical ID small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages | Route /paper/small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages

MCP example

{
  "tool": "get_paper",
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  }
}

source_context

{
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  "mode": "paper",
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  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

/buildability/small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages

Watchwatch

Subject: Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages

Paper ref

small-language-models-for-privacy-preserving-clinical-information-extraction-in-low-resource-languages

arXiv id

2602.21374

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

ea0b7763a88556f9d10a972f24017827c674b65a7eace2411f6a1266b8aa87b0

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

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Claim map

Constellation map

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Buzz

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References(27)

Related Papers

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Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline