AI in healthcare can save lives-or put them at risk. This episode explores guardrails, safety LLMs, regulation, and why generic AI controls fail in clinical settings.

Timestamps:

00:00 Introduction

01:26 What are LLM guardrails and why do they matter in healthcare

02:36 Why AI hallucinations are dangerous in medical settings

03:47 Why people still use chatbots for medical advice

05:13 Why generic AI safety tools fail in healthcare

06:16 Regulation pressure: US vs Europe

09:03 Guardrail frameworks: Guardrails AI, NeMo, Llama Guard

15:08 Safety LLMs and red teaming medical AI

22:17 Why healthcare AI needs application-specific testing

27:49 Shift-left AI safety and responsible design

32:44 The ELIZA effect

37:27 Practical advice for teams building healthcare AI

𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗳𝗼𝗿 𝗟𝗶𝘀𝘁𝗲𝗻𝗲𝗿𝘀 ►

Papers:

- Hakim et al. (2025) "The need for guardrails with large language models in pharmacovigilance."

- Meta's Llama Guard paper: "Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations" (arXiv:2312.06674)

- Ayala-Lauroba et al. (2024) "Enhancing Guardrails for Safe and Secure Healthcare AI" (arXiv:2409.17190)

Code and Models:

- Hakim et al. analysis code: https://github.com/jlpainter/llm-guardrails/

- Llama Guard: Available on Hugging Face (requires approval)

- gpt-oss-safeguard: https://huggingface.co/openai/gpt-oss-safeguard-20b (Apache 2.0)

Medical Ontologies:

- MedDRA (Medical Dictionary for Regulatory Activities): https://www.meddra.org/

- WHO Drug Dictionary: Via Uppsala Monitoring Centre

Regulatory Guidance:

- EMA AI Reflection Paper: https://www.ema.europa.eu/en/about-us/how-we-work/data-regulation-big-data-other-sources/artificial-intelligence

- FDA AI Guidance: Available on FDA.gov

LISTEN ON ►

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Apple Podcasts: https://podcasts.apple.com/us/podcast/the-healthcare-ai-podcast/id1827098175

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#HealthcareAI #AIGuardrails #MedicalAI #AISafety #AIEthics #HealthTech #AIRegulation #DigitalHealth #AIinMedicine #MLOps #AICompliance #AIHallucinations

Eugene Ugur Sayan is the Founder, CEO and President of Softheon, a technology company that powers healthcare.gov and state ACA exchanges serving over 10 million Americans daily.

Eugene filed a patent on intelligent software agents in 1998, long before anyone was discussing "agentic" workflows. Over the years, they built the Massachusetts Connector (America's first state health exchange) and now power the infrastructure behind healthcare.gov, serving over 10 million Americans with 1,300+ AI agents running 24/7/365.

This isn't theoretical AI. It's production systems where 99% accuracy means people lose health coverage. Eugene explains why healthcare demands "airline industry standards" (99.999% uptime), the PPT Framework (People, Process, Technology), how his team orchestrates agents across federal and 50-state AI regulations, and why Softheon owns its entire stack, from data centers to application layer technology.

Timestamps:

00:00 Opening & Introduction

02:22 The 1998 Patent: Building agentic workflows before ChatGPT

06:40 Why Healthcare AI Requires 99.999% Accuracy

10:00 Autonomy, Alignment & Accountability Framework

12:26 1,300 Semi-Autonomous Agents & Human-in-the-Loop (HITL)

18:49 Three Layers of AI: Hardware, Platform, Applications

23:09 Biggest Challenges: People, Process & Technology

31:04 Breaking Innovation Barriers in Conservative Healthcare

35:44 Transparency Rules & Value-Based Care

39:08 The ICHRA Revolution: Healthcare's 401K Moment

44:00 Consumer Engagement: Three Pillars Strategy

50:00 Entrepreneurship Philosophy & Daily Practice

53:00 Final Advice

Listen On:

YouTube: https://youtu.be/IWoARQ0G7sg

Apple Podcasts: https://podcasts.apple.com/us/podcast/the-healthcare-ai-podcast/id1827098175

Spotify: https://open.spotify.com/show/2XNrQBeCY7OGql2jVhcP7t

Amazon Music: https://music.amazon.com/podcasts/5b1f49a6-dba8-479e-acdf-9deac2f8f60e/the-healthcare-ai-podcast

You can learn more about the All-in-One Solution for Health Plans at Softheon.com

Follow Eugene Sayan:

LinkedIn: https://www.linkedin.com/in/eugenesayan/

Twitter: https://x.com/SayanEugene

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#AI #HealthcareAI #AgenticAI #HealthTech #HealthcareInnovation

In this episode of The Healthcare AI Pod, we unpack the impact of LLM-based medical agents on modern medicine – from architecture and multi-agent design to regulation and real-world risks.

Healthcare is facing a perfect storm: ageing populations, staff shortages, and rising costs. Can AI agents be the solution?

We discuss insights from over 60 studies on medical LLMs, including key areas such as:

• Multi-agent architectures and clinical decision support
• The security dilemma: protecting patient data when your API is just text
• Prompt injection attacks and HIPAA compliance challenges
• Liability concerns in AI-powered healthcare

From Baymax dreams to real-world implementation: how close are we?

Timestamps

0:00 Introduction – Baymax as inspiration for medical AI
2:20 What are LLM-based medical agents and how they differ from models
10:00 Healthcare security – regulation, compliance, and patient data
14:50 Patient reliance on AI, prompt-hacking, and global access challenges
18:00 Agent architectures – functional, role-based, and departmental approaches
25:10 Task decomposition and subject-matter expert input
28:00 Reward functions, accuracy vs user-pleasing bias, and physician training
33:00 User experience – agent personalities and conversational design
45:20 Liability, insurance, and evaluation of medical AI systems
54:20 Future outlook – Baymax revisited, challenges, and opportunities ahead

Mentioned Materials

• A Survey of LLM-based Agents in Medicine: How far are we from Baymax? https://arxiv.org/abs/2502.11211
• MAGDA: Multi-agent guideline-driven diagnostic assistance https://arxiv.org/abs/2409.06351

Listen On

• YouTube – https://youtu.be/R9h_Whj6sB0
• Apple Podcasts – https://podcasts.apple.com/us/podcast/the-healthcare-ai-podcast/id1827098175
• Spotify – https://open.spotify.com/show/2XNrQBeCY7OGql2jVhcP7t
• Amazon Music – https://music.amazon.com/podcasts/5b1f49a6-dba8-479e-acdf-9deac2f8f60e/the-healthcare-ai-podcast

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• Our Website – https://www.johnsnowlabs.com/
• LinkedIn – https://www.linkedin.com/company/johnsnowlabs/
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#HealthcareInnovation #AIAgents #HealthTech #MedicalAI #AIEthics #Baymax #MedicalLLM #HealthcareAI #ClinicalAI #MedicalTechnology #AIResearch #DigitalHealth #FutureOfMedicine #AIinMedicine #HealthcareAutomation #MedicalChatbots #PatientCare #HealthcareSolutions #MedicalInnovation