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
Benchmarks
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
### 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
### 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
- Research survey: Map clinical dialogue AI landscape
- Benchmark tracking: Compare medical AI performance
- System design: Learn from clinical agent architectures
- Safety analysis: Understand risks and mitigations
- Medical education: Patient simulation for training
References