Human Feedback Types
missingNone explicit
No explicit feedback protocol extracted.
"We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions."
HFEPX · Eval paper review
Doctorina MedBench: A Dialogue-Based Benchmark and Evaluation Framework for Agent-Based Medical AI
Anna Kozlova, Stanislau Salavei, Pavel Satalkin, Hanna Plotnitskaya +2 more
Published
Mar 26, 2026
Citations
0
Trust level
Moderate
Usefulness score
27/100 (Low)
Extraction confidence
50% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Domain Experts
Signals refreshed
Aug 13, 2026
Should you rely on this paper?
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this for comparison and orientation, not as your only source.
Best use
Background context only
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Validate the evaluation procedure and quality controls in the full paper before operational use.
Main weakness
No major weakness surfaced.
Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
27/100
Adjacent candidate
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
Abstract
We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions. Unlike traditional medical benchmarks that rely on solving standardized test questions, the proposed approach models a multi-step clinical dialogue in which an AI system must collect medical history, analyze available synthetic attachments when present, formulate differential diagnoses, and provide diagnostic and management recommendations. System performance is evaluated across separate task-level domains, including diagnosis, differential diagnosis, treatment, safety-critical condition handling, and dialogue-step behavior; the broader D.O.T.S. framework is used as a supplementary summary for diagnosis, observations/investigations, treatment, and step count. The framework also supports testing and quality-monitoring workflows intended to identify changes in model behavior during development. It supports safety-oriented cases, category-based sampling of synthetic clinical scenarios, and regression-style comparisons across system versions. In the reported study, the analyzed paired complete-case cohort consisted of 254 physician-authored synthetic clinical cases retained from 261 attempted case identifiers. The evaluation metrics are intended for comparative research on interactive medical AI systems and for studying clinical reasoning workflows in synthetic dialogue settings. Our results suggest that simulated clinical dialogue can provide a complementary assessment setting to traditional examination-style benchmarks, while the reported findings do not establish independent clinical validity, clinical effectiveness, or readiness for real-world deployment.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
strongSimulation Env
Includes extracted eval setup.
"We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions."
Quality Controls
missingNot reported
No explicit QC controls found.
"We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions."
Benchmarks / Datasets
strongMedbench
Useful for quick benchmark comparison.
"We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions."
Reported Metrics
missingNot extracted
No metric anchors detected.
"We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions."
Rater Population
strongDomain Experts
Helpful for staffing comparability.
"We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions."
Benchmarks and datasets
Medbench
Reported metrics
No metric terms were extracted from the available abstract.
Human feedback details
- Uses human feedback
- No
- Feedback types
- None
- Rater population
- Domain Experts
- Expertise required
- Medicine
Evaluation details
- Evaluation modes
- Simulation Env
- Agentic eval
- Long Horizon
- Quality controls
- Not reported
- Evidence quality
- Moderate
- Use this page as
- Background context only
Research brief
Metadata summary
We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions.
- Unlike traditional medical benchmarks that rely on solving standardized test questions, the proposed approach models a multi-step clinical dialogue in which an AI system must collect medical history, analyze available synthetic attachments when present, formulate differential diagnoses, and provide diagnostic and management recommendations.
- System performance is evaluated across separate task-level domains, including diagnosis, differential diagnosis, treatment, safety-critical condition handling, and dialogue-step behavior; the broader D.O.T.S.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Validate inferred eval signals (Simulation environment, Long-horizon tasks) against the full paper.
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.
Recommended queries
Contribution summary
- We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions.
- Unlike traditional medical benchmarks that rely on solving standardized test questions, the proposed approach models a multi-step clinical dialogue in which an AI system must collect medical history, analyze available synthetic attachments…
- System performance is evaluated across separate task-level domains, including diagnosis, differential diagnosis, treatment, safety-critical condition handling, and dialogue-step behavior; the broader D.O.T.S.
Why it matters for eval
- We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions.
- Unlike traditional medical benchmarks that rely on solving standardized test questions, the proposed approach models a multi-step clinical dialogue in which an AI system must collect medical history, analyze available synthetic attachments…
Researcher checklist
-
Human feedback protocol is explicit
No explicit human feedback protocol detected.
-
Evaluation mode is explicit
Detected: Simulation Env
-
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
-
Benchmark or dataset anchors are present
Detected: Medbench
-
Metric reporting is present
No metric terms extracted.