Benchmarking Multi-turn Medical Diagnosis: Hold, Lure, and Self-Correction
Benchmarking Multi-turn Medical Diagnosis: Hold, Lure, and Self-Correction explores This research introduces a benchmark and actionable guidance to improve the reliability of large language models in multi-turn medical diagnosis by addressing premature answering and leveraging self-correction.. Commercial viability score: 7/10 in Medical AI.
Use This Via API or MCP
Use the canonical paper page as a proof artifact
This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisCan Large Language Models Self-Correct in Medical Question Answering? An Exploratory StudyViability: 3.0
- Prior WorkMedClarify: An information-seeking AI agent for medical diagnosis with case-specific follow-up questionsViability: 7.0
- Prior WorkMedDialBench: Benchmarking LLM Diagnostic Robustness under Parametric Adversarial Patient BehaviorsViability: 7.0
- Higher ViabilityShattering the Shortcut: A Topology-Regularized Benchmark for Multi-hop Medical Reasoning in LLMsViability: 9.0
- Competing Approach