About this episode

The episode discusses the unpredictability of Large Language Models in the context of medical device development and safety.

Artificial intelligence has officially entered the mainstream cultural zeitgeist, creating a wave of excitement—and a fair share of fatigue—across the medical device industry. In this episode, host Etienne Nichols sits down with Tyler Harmon, biomedical engineer and CEO of Iaso Automated Medical Systems, to cut through the marketing buzzwords. Together, they explore the technical realities behind the technology stack, shifting the conversation away from generic AI toward specific, actionable engineering frameworks. The discussion highlights a critical distinction between traditional machine learning models and consumer-oriented Large Language Models (LLMs). Harmon explains that while technologies like convolutional neural networks (CNNs) have successfully processed medical imaging for years, modern LLMs introduce an intentional element of randomness to mimic human conversation. This lack of predictability presents unique challenges for medical device developers who operate in a deterministic, safety-critical environment where reproducibility is paramount. Looking toward practical deployment, the episode addresses how companies can responsibly govern these tools both within their…

People in this episode

Host: Etienne Nichols

Guest: Tyler Harmon

Topics covered

• artificial intelligence
• medical devices
• machine learning
• Large Language Models
• Quality Management Systems
• safety-critical environments

Keywords

• AI
• machine learning
• medical imaging
• deterministic systems
• software governance
• QMS
• SOUP

Mentioned in this episode

Organizations: Iaso Automated Medical Systems, IEC 62304

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