206 - Will AI Decide Your Next Drug/Device Approval?

Let's Combinate · 17 Sept 2025 · 31m

What this episode covers

Dominick Romano, founder of Drainpipe.io, explains how AI can be regulated in pharma and medtech by applying ICH guidelines and process validation to AI systems. He argues that pharmaceuticals are uniquely suited for AI because both deal with 'combinatorial problem sets' involving infinite inputs and outputs, requiring rigorous validation to prevent hallucinations in regulatory dossiers. Romano details how AI can accelerate dossier preparation by reducing millions of working hours in data acquisition and how regulators might use AI agents to speed up review processes, provided the technology is proven safe and accurate by the companies themselves.

Other shows on the same ground, closest first.

  • Global Medical Device Podcast · 24m

    #459: The Purolea Warning Letter & Validating AI in Medical Devices - What FDA Actually Requires

    Host Etienne Nichols analyzes the FDA's warning letter to Purolea Cosmetics Lab, arguing that the citation was not a crackdown on AI but a failure of quality system oversight and process validation. He explains that while Purolea used AI agents to draft specifications, the core violation was the lack of human review and accountability under 21 CFR 211.22. Nichols outlines a three-part validation framework for AI/ML-based Software as a Medical Device (SaMD), emphasizing the separation of training, validation, and holdout test data. He concludes by listing essential regulatory standards and five concrete questions to assess if a quality system is ready for FDA AI inspections.

  • Global Medical Device Podcast · 45m

    #441: ICYMI (In Case You Missed It!) How Artificial Intelligence is Impacting the MedTech Industry

    Ashkon Rasooli, a biomedical engineer and quality management expert, discusses the integration of AI in the medical device industry, highlighting the fundamental difference between deterministic software and statistical AI models. He details current applications in radiology, cardiology, and neurology, as well as wellness wearables like January AI and Whoop, while addressing critical challenges such as data bias and regulatory validation. Rasooli advocates for a staged adoption approach to build confidence in AI outcomes and emphasizes the importance of managing risks through robust quality management systems and emerging frameworks like the EU AI Act.

  • Global Medical Device Podcast · 44m

    Measuring the Impact of AI/ML Technologies on the Current Medical Device Landscape

    Mike Drues, president of Vascular Sciences, argues that the FDA's current focus on 'locked algorithms' stifles true AI in medical devices, citing only 29 cleared products as evidence of regulatory hesitation. He proposes three pragmatic solutions to enable adaptive AI: pre-validating a range of dynamic changes, allowing software to recommend changes for manufacturer approval, and enabling devices to learn collectively from a 'herd' of similar units. Drues contends that these engineering challenges have significant precedent in traditional device development, such as catheter sizing, and urges the industry to lead the conversation on good machine learning practices rather than waiting for FDA guidance.

  • Global Medical Device Podcast · 15m

    #460: FDA AI Regulations: Master the QA/RA Skills to Stay Ahead

    Etsy Nichols discusses the evolving FDA regulatory landscape for AI-enabled medical devices, highlighting the establishment of the Digital Health Center of Excellence and the shift toward rigorous documentation over algorithmic sophistication. She outlines five guiding principles for AI/ML-based Software as a Medical Device (SaMD) and emphasizes the critical importance of Predetermined Change Control Plans (PCCPs) in bridging US and EU regulatory requirements. Nichols argues that QA/RA professionals must combine regulatory fluency with AI literacy to navigate these complex systems effectively.

  • Global Medical Device Podcast · 24m

    #345: Using AI In MedTech

    Wade Schroeder, Medical Device Guru at Greenlight Guru, discusses the state of AI in MedTech, highlighting that 65% of professionals distrust AI data accuracy due to unknown sources. He explains how Greenlight Guru’s Risk Intelligence tool uses Bayesian statistics and natural language processing to pull data from the FDA’s MAUDE database, helping engineers address the 'cold start' problem in risk management. Schroeder also introduces the 'Digital Guru,' an AI bot trained specifically on FDA regulations to ensure compliance and trust, contrasting it with general models like ChatGPT.

The summary, chapters and topics are generated from the episode’s audio by this directory, not written by Let's Combinate. The episode itself belongs to its publisher.