Skip to content
← Back to explorer

HFEPX Hub

Multi Agent + Medicine (Last 60 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 10 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Frequently cited benchmark: Medpriv-Bench. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Mar 29, 2026.

Papers: 10 Last published: Mar 29, 2026 Global RSS Tag RSS
Multi AgentMedicineLast 60d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

High-Signal Coverage

100.0%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 0 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 40% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 80% of papers in this hub.
  • Medpriv-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly mixed annotation units; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • Medpriv-Bench appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 70% of hub papers (7/10); compare with a secondary metric before ranking methods.
  • agreement is reported in 10% of hub papers (1/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (40% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (10% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (80% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (80% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (0% vs 35% target).

Strengths

  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (0% coverage).
  • Benchmark coverage is thin (10% of papers mention benchmarks/datasets).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Track metric sensitivity by reporting both accuracy and agreement.
Recommended Queries (Expanded)

Recommended Queries

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

Mar 29, 2026

Yes Human Eval , Automatic Metrics Not Reported Accuracy Not Reported
ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

Mar 27, 2026

Yes Automatic Metrics Not Reported Accuracy Not Reported
A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment

Mar 23, 2026

Yes Automatic Metrics Not Reported Accuracy , Auroc Not Reported
MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-End Question Answering

Mar 15, 2026

No
Not Reported
Automatic Metrics Medpriv Bench Accuracy Not Reported
TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation

Feb 26, 2026

Yes Simulation Env Not Reported Not Reported Not Reported
Agentic Automation of BT-RADS Scoring: End-to-End Multi-Agent System for Standardized Brain Tumor Follow-up Assessment

Mar 23, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
RexDrug: Reliable Multi-Drug Combination Extraction through Reasoning-Enhanced LLMs

Mar 9, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy , Precision Not Reported
Efficient Failure Management for Multi-Agent Systems with Reasoning Trace Representation

Mar 23, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
MedMASLab: A Unified Orchestration Framework for Benchmarking Multimodal Medical Multi-Agent Systems

Mar 10, 2026

No
Not Reported
Automatic Metrics Not Reported Not Reported Not Reported
CCD-CBT: Multi-Agent Therapeutic Interaction for CBT Guided by Cognitive Conceptualization Diagram

Apr 8, 2026

No
Not Reported
Simulation Env Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal Improving Clinical Diagnosis with Counterfactual Mu… ClinicalAgents: Multi-Agent Orchestration for Clini… A Multidisciplinary AI Board for Multimodal Dementi…
Human Feedback Expert VerificationExpert VerificationExpert Verification
Evaluation Modes Human Eval, Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Not reportedNot reportedNot reported
Metrics AccuracyAccuracyAccuracy, Auroc
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsDomain Experts
Annotation Unit UnknownUnknownUnknown
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. CCD-CBT: Multi-Agent Therapeutic Interaction for CBT Guided by Cognitive Conceptualization Diagram

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Abstract: Large language models show potential for scalable mental-health support by simulating Cognitive Behavioral Therapy (CBT).

  2. Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + expert verification. Focus: accuracy. Abstract: Human evaluation further indicates that our framework produces more clinically.

  3. ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: While Large Language Models (LLMs) have demonstrated potential in healthcare,.

  4. TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + expert verification. Abstract: As mental health chatbots proliferate to address the global treatment gap,.

  5. A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: Modern clinical practice increasingly depends on.

  6. MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-End Question Answering

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: Medpriv-Bench / accuracy. Abstract: We establish a standardized evaluation protocol leveraging a pre-trained RoBERTa-Natural.

  7. Agentic Automation of BT-RADS Scoring: End-to-End Multi-Agent System for Standardized Brain Tumor Follow-up Assessment

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response.

  8. RexDrug: Reliable Multi-Drug Combination Extraction through Reasoning-Enhanced LLMs

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (0% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Expert Verification (4)

Evaluation Modes

  • Automatic Metrics (8)
  • Simulation Env (2)
  • Human Eval (1)

Top Benchmarks

  • Medpriv Bench (1)

Top Metrics

  • Accuracy (7)
  • Agreement (1)
  • Auroc (1)
  • Cost (1)

Rater Population Mix

  • Domain Experts (8)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 40.0% · benchmarks 10.0% · metrics 80.0% · quality controls 0.0%.

Top Papers

Related Hubs

Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.