Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications
📰 ArXiv cs.AI
Learn how to mitigate premature diagnostic handoff and silent hallucination in healthcare applications using an Agentic AI-based framework
Action Steps
- Build a multi-agent system using LLMs to detect and prevent premature diagnostic handoff
- Configure the system to identify and flag potential silent clinical hallucinations
- Test the framework using real-world healthcare data to evaluate its effectiveness
- Apply the framework to existing healthcare applications to improve diagnostic accuracy
- Compare the results with traditional diagnostic methods to assess the benefits of the Agentic AI-based approach
Who Needs to Know This
Data scientists and healthcare professionals can benefit from this framework to improve the reliability of AI-driven medical diagnosis
Key Insight
💡 Agentic AI-based frameworks can improve the reliability of AI-driven medical diagnosis by addressing critical failure modes
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💡 New Agentic AI-based framework mitigates premature diagnostic handoff and silent hallucination in healthcare applications #AIinHealthcare #MedicalDiagnosis
Key Takeaways
Learn how to mitigate premature diagnostic handoff and silent hallucination in healthcare applications using an Agentic AI-based framework
Full Article
Title: Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications
Abstract:
arXiv:2606.18068v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and multi-agent systems have driven the rise of Agentic AI, showing promise for medical reasoning. However, open-ended conversational agents remain prone to two critical failure modes: premature diagnostic handoff and silent clinical hallucinations that may go undetected before reaching the patient. In this work, we propose a multi-agent framework that addresses both issues by replacing ``LLM-as-a-jud
Abstract:
arXiv:2606.18068v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and multi-agent systems have driven the rise of Agentic AI, showing promise for medical reasoning. However, open-ended conversational agents remain prone to two critical failure modes: premature diagnostic handoff and silent clinical hallucinations that may go undetected before reaching the patient. In this work, we propose a multi-agent framework that addresses both issues by replacing ``LLM-as-a-jud
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