ARXIV:2602.20130 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

arXiv

Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy. Methods: We propose Selective Chain-of-Thought (Selective CoT), an inference-time strategy that first predicts whether…

METHOD

Full abstract

Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy. Methods: We propose Selective Chain-of-Thought (Selective CoT), an inference-time strategy that first predicts whether a question requires reasoning and generates a rationale only when needed. Two open-source LLMs (Llama-3.1-8B and Qwen-2.5-7B) were evaluated on four biomedical QA benchmarks-HeadQA, MedQA-USMLE, MedMCQA, and PubMedQA. Metrics included accuracy, total generated tokens, and inference time. Results: Selective CoT reduced inference time by 13-45% and token usage by 8-47% with minimal accuracy loss ($\leq$4\%). In some model-task pairs, it achieved both higher accuracy and greater efficiency than standard CoT. Compared with fixed-length CoT, Selective CoT reached similar or superior accuracy at substantially lower computational cost. Discussion: Selective CoT dynamically balances reasoning depth and efficiency by invoking explicit reasoning only when beneficial, reducing redundancy on recall-type questions while preserving interpretability. Conclusion: Selective CoT provides a simple, model-agnostic, and cost-effective approach for medical QA, aligning reasoning effort with question complexity to enhance real-world deployability of LLM-based clinical systems.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.

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

Opportunity summary

Score7.0

PainSelective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

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

ARXIV:2602.20130 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

arXiv

Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

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 7.0/10.

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

Opportunity summary

Score7.0

PainSelective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

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

Paper Pack

10.48550/arXiv.2602.20130

To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

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 fallback23 indexednot indexed

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

Viability

7.0

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 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy. Methods: We propose Selective Chain-of-Thought (Selective CoT), an inference-time strategy that first predicts whether a question requires reasoning and generates a rationale only when needed.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy. Methods: We propose Selective Chain-of-Thought (Selective CoT), an inference-time strategy that first predicts whether a question requires reasoning and generates a rationale only when needed.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy.
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 7.0/10.
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

Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.20130 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(23)

Benchmarking GPT-5 for biomedical natural language processing

2025Yu Hou, Zaifu Zhan et al.

MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering

2025Ziyu Wang, Elahe Khatibi et al.

Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications

2025Wenxuan Wang, Zizhan Ma et al.

A comparative evaluation of chain-of-thought-based prompt engineering techniques for medical question answering

2025Sohyeon Jeon, Hong-Ki Kim

Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs

2025Chenqian Le, Ziheng Gong et al.

A systematic review of large language model (LLM) evaluations in clinical medicine

2025Sina Shool, Sara Adimi et al.

An evaluation of DeepSeek Models in Biomedical Natural Language Processing

2025Zaifu Zhan, Shuang Zhou et al.

The Large Language Models on Biomedical Data Analysis: A Survey

2025Wei Lan, Zhentao Tang et al.

BioMedGPT: An Open Multimodal Large Language Model for BioMedicine

2024Yizhen Luo, Jiahuan Zhang et al.

Large Language Models for Disease Diagnosis: A Scoping Review

2024Shuang Zhou, Zidu Xu et al.

The Llama 3 Herd of Models

2024Abhimanyu Dubey, Abhinav Jauhri et al.

Qwen2 Technical Report

2024An Yang, Baosong Yang et al.

Reasoning with large language models for medical question answering

2024Mary M. Lucas, Justin Yang et al.

MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning

2024Shuyue Stella Li, Vidhisha Balachandran et al.

KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques

2024Rui Yang, Haoran Liu et al.

A Domain-Specific Next-Generation Large Language Model (LLM) or ChatGPT is Required for Biomedical Engineering and Research

2023S. Pal, M. Bhattacharya et al.

Can large language models reason about medical questions?

2022Valentin Li'evin, C. Hother et al.

MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering

2022Ankit Pal, Logesh Kumar Umapathi et al.

Chain of Thought Prompting Elicits Reasoning in Large Language Models

2022Jason Wei, Xuezhi Wang et al.

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

2020Di Jin, Eileen Pan et al.

Showing 20 of 23 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 WorkLearning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning

7.0

Prior WorkCoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs

7.0

Prior WorkCoT2-Meta: Budgeted Metacognitive Control for Test-Time Reasoning

7.0

Prior WorkSmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning

7.0

Extension

Builds On ThisCan Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study

3.0

Builds On ThisDual-Track CoT: Budget-Aware Stepwise Guidance for Small LMs

3.0

Builds On ThisFrom Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning

3.0

Builds On ThisThe Cost of Reasoning: Chain-of-Thought Induces Overconfidence in Vision-Language Models

2.0

Commercially relevant

Higher ViabilityHierarchical Chain-of-Thought Prompting: Enhancing LLM Reasoning Performance and Efficiency

8.0

Conflicting

Competing ApproachWhen Chain-of-Thought Backfires: Evaluating Prompt Sensitivity in Medical Language Models

7.0

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

Subscribe to the weekly brief

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: 2602.20130
Route: /paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering
Active tab: read
Artifact: to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering/paper-pack
REST build passport API/api/v1/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering/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

{
  "contract_version": "paper-r2",
  "paper_id": "f965d06e-ad95-4b1e-b847-d002c8763f79",
  "arxiv_id": "2602.20130",
  "canonical_route": "/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering",
  "endpoints": {
    "paper_pack": "/api/v1/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering/paper-pack",
    "build_passport": "/api/v1/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
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

To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

Canonical ID to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering | Route /paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.20130"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering",
  "normalized_query": "2602.20130",
  "route": "/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering",
  "paper_ref": "to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering",
  "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: To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

/buildability/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering

Watchwatch

Subject: To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

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/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering

Paper ref

to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering

arXiv id

2602.20130

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

ee719b0d6179c7f99052dde8b43e419f7a83593bdf88fa339cbc7885249ab429

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.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering#webpage",
      "url": "https://sciencetostartup.com/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering",
      "name": "To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering",
      "description": "Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering#scholarlyArticle",
      "headline": "To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering",
      "description": "Selective CoT enhances real-world deployability of medical QA systems by optimizing reasoning processes in LLMs, reducing costs while maintaining accuracy.",
      "url": "https://sciencetostartup.com/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering",
      "sameAs": "https://arxiv.org/abs/2602.20130",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2602.20130"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-02-23T18:42:50.000Z",
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "0dde6585acb2bbc202b9f8e9276e70a91826d033"
          },
          "url": "https://www.semanticscholar.org/paper/0dde6585acb2bbc202b9f8e9276e70a91826d033"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f44d5a3bf469345d896ab882142af5cfaa3971b9"
          },
          "url": "https://www.semanticscholar.org/paper/f44d5a3bf469345d896ab882142af5cfaa3971b9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "bdd3ab297cc1066c800c292ecb58d65bd2b090e9"
          },
          "url": "https://www.semanticscholar.org/paper/bdd3ab297cc1066c800c292ecb58d65bd2b090e9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a21a0fd89c4906896c16a0dea8d4e49e18fd054c"
          },
          "url": "https://www.semanticscholar.org/paper/a21a0fd89c4906896c16a0dea8d4e49e18fd054c"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "13b6c2d6a35f7ef3bfba9de00ff332b0eb889ecf"
          },
          "url": "https://www.semanticscholar.org/paper/13b6c2d6a35f7ef3bfba9de00ff332b0eb889ecf"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "705a913fd7153069b99a66c03862657122d9f737"
          },
          "url": "https://www.semanticscholar.org/paper/705a913fd7153069b99a66c03862657122d9f737"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "b0636914bc9870e02861b760a489fa542cca1bd4"
          },
          "url": "https://www.semanticscholar.org/paper/b0636914bc9870e02861b760a489fa542cca1bd4"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "88620b06b8b13682db2d3f699e1cda3fee5ca2db"
          },
          "url": "https://www.semanticscholar.org/paper/88620b06b8b13682db2d3f699e1cda3fee5ca2db"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "7b2c74fd15cd9c4c942106df381139b20dc38df7"
          },
          "url": "https://www.semanticscholar.org/paper/7b2c74fd15cd9c4c942106df381139b20dc38df7"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "525018c1cc3d3404e8703266a1564814789fa309"
          },
          "url": "https://www.semanticscholar.org/paper/525018c1cc3d3404e8703266a1564814789fa309"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "40e8af970329135ec95057d73e239dab805ad128"
          },
          "url": "https://www.semanticscholar.org/paper/40e8af970329135ec95057d73e239dab805ad128"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "54fb839f621e3fe787437ab8ca5f37e7e4726bfe"
          },
          "url": "https://www.semanticscholar.org/paper/54fb839f621e3fe787437ab8ca5f37e7e4726bfe"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "26d8487f05024425e0a87292ba1332e6ee34ef03"
          },
          "url": "https://www.semanticscholar.org/paper/26d8487f05024425e0a87292ba1332e6ee34ef03"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "2a9a2e0e3cc82e8277d9daf824ac5ea6d6584b6f"
          },
          "url": "https://www.semanticscholar.org/paper/2a9a2e0e3cc82e8277d9daf824ac5ea6d6584b6f"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "13f94fb16fd719769e8b5215035b231d1211f9fa"
          },
          "url": "https://www.semanticscholar.org/paper/13f94fb16fd719769e8b5215035b231d1211f9fa"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "467c49fd4f69638ddeda1ff3241fe4495151cf0e"
          },
          "url": "https://www.semanticscholar.org/paper/467c49fd4f69638ddeda1ff3241fe4495151cf0e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d697b440dd0e65a05fe027e4c0ea85f62dcba033"
          },
          "url": "https://www.semanticscholar.org/paper/d697b440dd0e65a05fe027e4c0ea85f62dcba033"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "741776172685b9717159a9fcd21841461bb33b14"
          },
          "url": "https://www.semanticscholar.org/paper/741776172685b9717159a9fcd21841461bb33b14"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1b6e810ce0afd0dd093f789d2b2742d047e316d5"
          },
          "url": "https://www.semanticscholar.org/paper/1b6e810ce0afd0dd093f789d2b2742d047e316d5"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "fc97c3f375c7228a1df7caa5c0ce5d2a6a171bd7"
          },
          "url": "https://www.semanticscholar.org/paper/fc97c3f375c7228a1df7caa5c0ce5d2a6a171bd7"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 7
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "Medical AI"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Medical AI",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "To Reason or Not to: Selective Chain-of-Thought in Medical Q",
          "item": "https://sciencetostartup.com/paper/to-reason-or-not-to-selective-chain-of-thought-in-medical-question-answering"
        }
      ]
    }
  ]
}

To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(23)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(23)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline