Episode
620 - From Know-What to Know-How: Medcast’s Journey with Medical-Grade AI at DHF26
- Podcast
- Talking HealthTech
- Published
- Jul 1, 2026
- Duration seconds
- 1281
- Processing state
not_requested- Canonical source
- https://www.talkinghealthtech.com/podcast
Actions
POST https://stenobird.com/v1/public/podcasts/talking-healthtech-773483/episodes/620-from-know-what-to-know-how-medcast-s-journey-with-medical-grade-ai-at-dhf26/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/talking-healthtech-773483/620-from-know-what-to-know-how-medcast-s-journey-with-medical-grade-ai-at-dhf26.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
Find out how Medcast is building trustworthy medical-grade AI in this episode of Talking HealthTech, recorded live at the Digital Health Festival 2026 in Melbourne, Australia's premier gathering for health innovation, technology, and policy. Peter Birch sits down with Dr Stephen Barnett, CEO of Medcast, to explore what it genuinely takes to make AI safe, reliable, and fit for purpose in a clinical setting. With a background spanning general practice, research, and digital health innovation, Barnett brings a grounded and practical perspective to one of the most talked-about topics in healthcare right now. Together, they unpack the concept of knowledge translation, the persistent gap between cutting-edge AI and trustworthy clinical tools, and why large language models alone are not enough. The conversation covers retrieval-augmented generation, the risks of AI hallucinations, the importance of curated and validated data sources, and the orchestration layer that sits between a powerful model and a safe medical answer. They also discuss how clinical workflows are changing, the shift in how doctors use digital tools in front of patients, and the emerging conversation around patient-facing AI. Key Takeaways 🧑⚕️ Knowledge translation in healthcare relies on getting the right information to the right person at the right time, impacting both clinician learning and patient outcomes. 🤖 Large language models require an orchestration layer and data curation processes to be considered "medical grade" and trustworthy for clinical use. 🔎 Retrieval-augmented generation and other governance measures help address hallucinations and bias in AI-powered healthcare solutions, supporting quality, safety, and auditability. 💡 Medluma, Medcast's AI platform, addresses compliance, standardisatio…