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ARXIV:2605.02240 · MEDICAL AI AGENTS · SUBMITTED 05 MAY · 20:28 UTC · FRESHNESS STALE
ARXIV:2605.02240MEDICAL AI AGENTSSUBMITTED 05 MAY · 20:28 UTCFRESHNESS STALERuoqi Liu · Imran Q. Mohiuddin · Austin J. Schoeffler · Kavita Renduchintala · Ashwin Nayak · Prasantha L. Vemu · +7 at arXiv
PhysicianBench is a benchmark for evaluating LLM agents on real-world EHR tasks, revealing a significant gap in current capabilities for autonomous clinical agents.
Opportunity summary
Pain PhysicianBench is a benchmark for evaluating LLM agents on real-world EHR tasks, revealing a significant gap in current capabilities for autonomous clinical agents.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
PhysicianBench is a benchmark for evaluating LLM agents on real-world EHR tasks, revealing a significant gap in current capabilities for autonomous clinical agents. Existing medical agent benchmarks primarily focus on static knowledge recall, single-step…
We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record (EHR) environments. Existing medical agent benchmarks primarily focus on static knowledge recall, single-step…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, they fail to capture the long-horizon, composite workflows that characterize real clinical systems. Code availability is flagged in the production record;…
Medical AI Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
PhysicianBench is a benchmark for evaluating LLM agents on real-world EHR tasks, revealing a significant gap in current capabilities for autonomous clinical agents.
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Paper Pack
10.48550/arXiv.2605.02240PhysicianBench is a benchmark for evaluating LLM agents on real-world EHR tasks, revealing a significant gap in current capabilities for autonomous clinical agents.
Abstract
We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record (EHR) environments. Existing medical agent benchmarks primarily focus on static knowledge recall, single-step atomic actions, or action intent without verifiable execution against the environment. As a result, they fail to capture the long-horizon, composite workflows that characterize real clinical systems. PhysicianBench comprises 100 long-horizon tasks adapted from real consultation cases between primary care and subspecialty physicians, with each task independently reviewed by a separate panel of physicians. Tasks are instantiated in an EHR environment with real patient records and accessed through the same standard APIs used by commercial EHR vendors. Tasks span 21 specialties (e.g., cardiology, endocrinology, oncology, psychiatry) and diverse workflow types (e.g., diagnosis interpretation, medication prescribing, treatment planning), requiring an average of 27 tool calls per task. Solving each task requires retrieving data across encounters, reasoning over heterogeneous clinical information, executing consequential clinical actions, and producing clinical documentation. Each task is decomposed into structured checkpoints (670 in total across the benchmark) capturing distinct stages of completion graded by task-specific scripts with execution-grounded verification. Across 13 proprietary and open-source LLM agents, the best-performing model achieves only 46% success rate (pass@1), while open-source models reach at most 19%, revealing a substantial gap between current agent capabilities and the demands of real-world clinical workflows. PhysicianBench provides a realistic and execution-grounded benchmark for measuring progress toward autonomous clinical agents.
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Proof status
unverified0 refs; 3 sources; 50% 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
PhysicianBench is a benchmark for evaluating LLM agents on real-world EHR tasks, revealing a significant gap in current capabilities for autonomous clinical agents. Existing medical agent benchmarks primarily focus on static knowledge recall, single-step atomic actions, or actio...
METHOD
We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record (EHR) environments. Existing medical agent benchmarks primarily focus on static knowledge recall, single-step atomic actions, o...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, they fail to capture the long-horizon, composite workflows that characterize real clinical systems. Code availability is flagged in the production record; the public repository link still nee...
WHY NOW
Medical AI Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
PhysicianBench is a benchmark for evaluating LLM agents on real-world EHR tasks, revealing a significant gap in current capabilities for autonomous clinical agents. Existing medical agent benchmarks primarily focus on static knowledge recall, single-step atomic actions, or action intent without verifiable execution against the environment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record (EHR) environments. Existing medical agent benchmarks primarily focus on static knowledge recall, single-step atomic actions, or action intent without verifiable execution against the environment.
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. As a result, they fail to capture the long-horizon, composite workflows that characterize real clinical systems. 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 Agents 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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Concepts
Methods
Materials
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Competitors
PhysicianBench is a benchmark for evaluating LLM agents on real-world EHR tasks, revealing a significant gap in current capabilities for autonomous clinical agents.
Segment
Medical AI Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Foundation
Commercially relevant
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 3 sources, 50% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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No CRM or outreach source attached.
People
No named person assigned.
Gaps
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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