clinical-dialogue-agents-guide
Papers on AI agents for clinical dialogue and medical QA
Best use case
clinical-dialogue-agents-guide is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Papers on AI agents for clinical dialogue and medical QA
Teams using clinical-dialogue-agents-guide should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/clinical-dialogue-agents-guide/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How clinical-dialogue-agents-guide Compares
| Feature / Agent | clinical-dialogue-agents-guide | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Papers on AI agents for clinical dialogue and medical QA
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Agentic Clinical Dialogue Guide
## Overview
A curated collection of papers on AI agents for clinical dialogue — systems that conduct patient interviews, perform differential diagnosis, explain medical information, and support clinical decision-making through conversation. Covers medical QA benchmarks, patient simulation, clinical reasoning chains, and safety considerations unique to healthcare AI.
## Research Landscape
```
Agentic Clinical Dialogue
├── Patient-Facing Agents
│ ├── Symptom checkers
│ ├── Triage systems
│ ├── Health information
│ └── Follow-up management
├── Clinician-Facing Agents
│ ├── Diagnostic support
│ ├── Treatment recommendation
│ ├── Clinical documentation
│ └── Literature integration
├── Clinical Reasoning
│ ├── Differential diagnosis
│ ├── History taking
│ ├── Physical exam interpretation
│ └── Test ordering
├── Patient Simulation
│ ├── Standardized patients (SP)
│ ├── Medical education
│ └── Agent evaluation
└── Safety & Ethics
├── Hallucination in medicine
├── Bias in clinical AI
├── Liability frameworks
└── Informed consent
```
## Key Systems
| System | Focus | Approach |
|--------|-------|----------|
| **AMIE** | Diagnostic dialogue | LLM with clinical reasoning |
| **Med-PaLM** | Medical QA | Finetuned on medical data |
| **ChatDoctor** | Patient consultation | LLaMA + medical knowledge |
| **AgentClinic** | Clinical evaluation | Simulated clinical encounters |
| **ClinicalAgent** | Decision support | Multi-step clinical reasoning |
## Benchmarks
```python
benchmarks = {
"MedQA (USMLE)": {
"task": "US Medical Licensing Exam questions",
"size": "11,450 questions",
"metric": "Accuracy",
},
"PubMedQA": {
"task": "Biomedical yes/no/maybe QA",
"size": "1,000 expert-labeled",
"metric": "Accuracy",
},
"AgentClinic": {
"task": "Simulated clinical encounters",
"size": "Various patient scenarios",
"metric": "Diagnostic accuracy + safety",
},
"MedMCQA": {
"task": "Indian medical entrance MCQs",
"size": "194k questions",
"metric": "Accuracy",
},
"HealthSearchQA": {
"task": "Consumer health search questions",
"size": "3,375 questions",
"metric": "Expert evaluation",
},
}
for name, info in benchmarks.items():
print(f"\n{name}:")
print(f" Task: {info['task']}")
print(f" Size: {info['size']}")
```
## Safety Considerations
```markdown
### Critical Safety Issues
1. **Hallucination** — Fabricated medical facts are dangerous
2. **Scope limitations** — AI must know when to defer to human
3. **Emergency recognition** — Must identify urgent situations
4. **Bias** — Demographic biases in training data
5. **Liability** — Legal framework for AI medical advice
6. **Privacy** — Patient data protection (HIPAA compliance)
### Safety Patterns
- Always recommend consulting healthcare providers
- Flag emergency symptoms immediately
- Disclose AI nature to patients
- Log all interactions for audit
- Implement uncertainty quantification
```
## Reading Roadmap
```markdown
### Foundations
1. AMIE: "Towards Conversational Diagnostic AI" (Google, 2024)
2. Med-PaLM 2: "Expert-level medical QA" (Google, 2023)
3. "Evaluating LLMs in Clinical Dialogue" (Survey, 2024)
### Clinical Reasoning
4. "Chain-of-Diagnosis" (Clinical CoT, 2024)
5. "AgentClinic: Evaluating Clinical Agents" (2024)
6. "Simulated Patient Encounters with LLMs" (2024)
### Safety
7. "Hallucination in Medical AI" (Survey, 2024)
8. "Red Teaming Medical LLMs" (2024)
```
## Use Cases
1. **Research survey**: Map clinical dialogue AI landscape
2. **Benchmark tracking**: Compare medical AI performance
3. **System design**: Learn from clinical agent architectures
4. **Safety analysis**: Understand risks and mitigations
5. **Medical education**: Patient simulation for training
## References
- [Awesome-Agentic-Clinical-Dialogue](https://github.com/xqz614/Awesome-Agentic-Clinical-Dialogue)
- [AMIE Paper](https://arxiv.org/abs/2401.05654)
- [AgentClinic](https://arxiv.org/abs/2405.07960)Related Skills
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