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ARXIV:2603.25196 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.25196MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEAndong Tan · Shuyu Dai · Jinglu Wang · Fengtao Zhou · Yan Lu · Xi Wang · +4 at arXiv
A benchmark to evaluate and improve LLM adherence to clinical practice guidelines, crucial for safe healthcare deployment.
Opportunity summary
Pain A benchmark to evaluate and improve LLM adherence to clinical practice guidelines, crucial for safe healthcare deployment.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A benchmark to evaluate and improve LLM adherence to clinical practice guidelines, crucial for safe healthcare deployment. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend…
Clinical practice guidelines (CPGs) play a pivotal role in ensuring evidence-based decision-making and improving patient outcomes. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Given that each clinical recommendation may affect a large population and that clinical applications are inherently safety critical, addressing these gaps is crucial for…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A benchmark to evaluate and improve LLM adherence to clinical practice guidelines, crucial for safe healthcare deployment.
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10.48550/arXiv.2603.25196A benchmark to evaluate and improve LLM adherence to clinical practice guidelines, crucial for safe healthcare deployment.
Abstract
Clinical practice guidelines (CPGs) play a pivotal role in ensuring evidence-based decision-making and improving patient outcomes. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs could identify and adhere to CPGs during conversations. To address this gap, we introduce CPGBench, an automated framework benchmarking the clinical guideline detection and adherence capabilities of LLMs in multi-turn conversations. We collect 3,418 CPG documents from 9 countries/regions and 2 international organizations published in the last decade spanning across 24 specialties. From these documents, we extract 32,155 clinical recommendations with corresponding publication institute, date, country, specialty, recommendation strength, evidence level, etc. One multi-turn conversation is generated for each recommendation accordingly to evaluate the detection and adherence capabilities of 8 leading LLMs. We find that the 71.1%-89.6% recommendations can be correctly detected, while only 3.6%-29.7% corresponding titles can be correctly referenced, revealing the gap between knowing the guideline contents and where they come from. The adherence rates range from 21.8% to 63.2% in different models, indicating a large gap between knowing the guidelines and being able to apply them. To confirm the validity of our automatic analysis, we further conduct a comprehensive human evaluation involving 56 clinicians from different specialties. To our knowledge, CPGBench is the first benchmark systematically revealing which clinical recommendations LLMs fail to detect or adhere to during conversations. Given that each clinical recommendation may affect a large population and that clinical applications are inherently safety critical, addressing these gaps is crucial for the safe and responsible deployment of LLMs in real world clinical practice.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
A benchmark to evaluate and improve LLM adherence to clinical practice guidelines, crucial for safe healthcare deployment. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs could identify and adhere to CPGs d...
METHOD
Clinical practice guidelines (CPGs) play a pivotal role in ensuring evidence-based decision-making and improving patient outcomes. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs could identify and adhere t...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Given that each clinical recommendation may affect a large population and that clinical applications are inherently safety critical, addressing these gaps is crucial for the safe and responsible deploymen...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A benchmark to evaluate and improve LLM adherence to clinical practice guidelines, crucial for safe healthcare deployment. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs could identify and adhere to CPGs during conversations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Clinical practice guidelines (CPGs) play a pivotal role in ensuring evidence-based decision-making and improving patient outcomes. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs could identify and adhere to CPGs during conversations.
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. Given that each clinical recommendation may affect a large population and that clinical applications are inherently safety critical, addressing these gaps is crucial for the safe and responsible deployment of LLMs in real world clinical practice. 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 April 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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A benchmark to evaluate and improve LLM adherence to clinical practice guidelines, crucial for safe healthcare deployment.
Segment
Medical AI
Adoption evidence
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Commercial read
7.0/10 public viability
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missing
reason
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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passport absent
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stale
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Technical feasibility
partial
Current read
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Gaps
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missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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