Dev and Doc: AI For Healthcare Podcast

2026 is going to be a big year. 2025 was the year of AI agents, voice, and more intelligent autonomous large language models. Now, some massive changes are coming — including big tech's grab for healthcare, the rapid progression of robotics, and new world models that will usher in a new era of AI applications.
Join academic and industry experts Dev and Doc as they delve into the biggest predictions in AI healthcare for 2026. You heard it here first! :)
👋 Hey! If you are enjoying our conversations, reach out and share your thoughts and journey with us. Don't forget to subscribe whilst you're here!
— Timestamps —
00:00 Intro
01:02 What are you using AI for right now?
11:43 AI Scribe wars: Who will win?
14:44 Which Big Tech will lead 2026?
16:52 Isomorphic Labs and AI drugs
18:09 Healthcare grab from big tech companies
22:54 Self-play models on the rise
26:48 Will Academia contribute more?
28:15 2026: The year of world models (and what it means for us)
30:36 Robotics advancements in 2026
32:43 Digital twins (coming from us, hopefully!)
33:00 Will a breakthrough change what we do?
35:45 The fall of Hippocratic AI
— Meet the Hosts —
👨🏻‍⚕️ Doc: Dr. Joshua Au Yeung - LinkedIn
🤖 Dev: Zeljko Kraljevic - X (Twitter)
— Connect with Us —
📺 YouTube: DevAndDoc
📻 Spotify: Listen Here
🍎 Apple Podcasts: Listen Here
📧 Substack: Read our Newsletter
For enquiries: 📧 [email protected]
— Credits —
🎞️ Editor: Dragan Kraljević - Instagram
🎨 Brand Design: Ana Grigorovici - Behance

Reviewing Dev & Doc's 2024/2025 AI Healthcare Predictions.
What a year it's been! In this episode of Dev & Doc, we look back at the predictions we made almost 2 years ago. What did we get right? (And what AI developments did we completely overlook that occurred in 2025?)
📺 Watch where it all began: Our Original 2024 AI Predictions Episode
It's going to be a fun one :) What are your predictions for 2026? Let us know!
👋 Hey! If you are enjoying our conversations, reach out, share your thoughts and journey with us. Don't forget to subscribe whilst you're here!
Timestamps:
00:00 Highlight
01:06 Ambient: Biggest game changer
04:01 Open Source will catch up to closed source
09:20 Big AI companies will fail
10:52 There will be more trials involving large language models
13:55 Industry will lead progress
19:36 LLMs are not going to replace therapists or doctors
22:17 AI psychosis and big tech
27:19 People with AI replace people without AI
29:12 Radiology AI will become more widespread
31:00 Dev was way too optimistic about OpenAI; Google is coming for you
33:05 Predictions we missed: GOOGLE KILLED EVERYONE
37:10 Uprising of China Open source, xAI
39:15 RAG-based search products like OpenEvidence, MedWise (UK), Prof Valmed
The Team:
👨🏻‍⚕️ Doc - Dr. Joshua Au Yeung: LinkedIn
🤖 Dev - Zeljko Kraljevic: Twitter/X
References:
• Nuraxi: https://www.nuraxi.ai/
• EU's Earth twin: https://destination-earth.eu/
• Blog on language representation of biology: Read here
• Foresight GPT: The Lancet
Connect With Us:
📺 YouTube
🍎 Apple Podcasts
✉️ Substack
📧 Enquiries: [email protected]
Credits:
🎞️ Editor: Dragan Kraljević (Instagram)
🎨 Brand Design: Ana Grigorovici (Behance)

In this episode of Dev and Doc, we deep dive into the world of Digital Twins. Popularised in engineering, we explore key concepts and ideas before looking to the future: how we can combine digital twins with today's powerful AI /GPT-based models (LLMs) and healthcare data to bring on a new revolution of healthcare to the world.
This means the chance for every single person to create digital twins of themselves where they can understand their personal health, risks, disease trajectories, and treatment outcomes by simulating the future. This is the true promise of precision medicine for all. Crazy, right?
Dev and Doc recently joined forces to build this exact vision in their start-up, Nuraxi.
🚀 Nuraxi is a deep-tech company focused on advancing health and precision medicine through artificial intelligence and digital twin technology.
https://www.nuraxi.ai/
👋 Hey! If you are enjoying our conversations, reach out, share your thoughts and journey with us. Don't forget to subscribe whilst you're here :)
Timestamps:
00:00 - Intro: Digital Twin (DT)
01:22 - Start / Introduction to DT
08:28 - Levels of DTs
18:33 - Using natural language to capture biology complexities and scales
26:45 - First time in humanity: Combination of AI, compute, healthcare data, and wearables
33:15 - Building Agentic Health Twins at Nuraxi
38:15 - Combining AI and Digital Twins: GPT-based simulations of the future
44:20 - To change healthcare, we must be able to predict the future
49:10 - Future directions: From molecular and organ twins to Population Twins
The Hosts:
👨🏻‍⚕️ Doc - Dr. Joshua Au Yeung
LinkedIn Profile

🤖 Dev - Zeljko Kraljevic
Twitter Profile
References:
• Nuraxi: Website
• EU's Earth Twin: Destination Earth
• Blog on language representation of biology: Read here
• Foresight GPT (The Lancet): Read Paper
Listen & Subscribe:
📺 YouTube
🎧 Spotify
🍏 Apple Podcasts
📝 Substack
Credits:
📧 Enquiries: [email protected]
🎞️ Editor: Dragan Kraljević (Instagram)
🎨 Brand Design: Ana Grigorovici (Behance)

Join Josh and Zeljko live at HETT 2025 in London - covering the most exciting topics and highlights that are upcoming in AI for healthcare. Coming from the duo who are living and breathing AI for healthcare, and together, have worked across every area of healthTech - from the hospital frontlines, to university research, to NHS implementation, to building industry grade agents including AI scribes, computer control and digital twins, to product and compliance. This is one not to miss!
00:00 start and intro
2:15 What are AI agents? (and why they're different from chatbots)
3:52 AI scribes: the 150 company sprint to "scribe plus" features
8:02 AI psychosis and mental health - all LLMs reinforce delusional beliefs
9:34 Computer control: Automating hospital workflows by mimicking human actions
13:42 Digital twins for health are the future: A safer path forward?
18:40 How does the national health service become AI enabled?
22:22 closing remarks - Is AI in healthcare a hype or hope?
25:12 questions - digital twins for individuals or for cohorts?
26:52 questions - Lessons from building AVTs and digital twins for consumer space
29:02 questions - LLM clinical summarisation - risks and benefits
31:17 questions - ethics of AI vs Human errors. is it the same?
33:02 questions - challenges and barriers to AI deployment in NHS
👋 Hey! If you are enjoying our conversations, reach out, share your thoughts and journey with us. Don't forget to subscribe whilst you're here :)
👨🏻⚕️ Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/
🤖 Dev - Zeljko Kraljevic - https://twitter.com/zeljkokr
Follow us:
YT - https://youtube.com/@DevAndDoc
Spotify - https://podcasters.spotify.com/pod/show/devanddoc
Apple - https://podcasts.apple.com/gb/podcast/dev-and-doc-ai-for-healthcare-podcast/id1751495120
Substack - https://aiforhealthcare.substack.com/
For enquiries:
📧 [email protected]
Credits:
🎞️ Editor - Dragan Kraljević - https://www.instagram.com/dragan_kraljevic/
🎨 Brand design and art direction - Ana Grigorovici - https://www.behance.net/anagrigorovici027d

Whenever there was AI, there were benchmarks- from the turing test, to society-changing benchmarks like MNIST and ImageNet to modern problems like the ARC prize, benchmarked served a vital purpose to measure the performance of AI models. But something has shifted in modern times, in the LLM era have benchmarks lost their utility, becoming mere advertisement for big tech?
Even seemingly more sophisticated benchmarks like LM Arena can be gamed by tech giants. We also deep dive into healthcare benchmarks like OpenAI's Healthbench (deeply problematic) and Microsoft's AI-DXO orchestrator agent for diagnosis. Where is this all going? How do we make the perfect benchmark? Or is the real work to be done afterwards in the real world?
👋 Hey! If you are enjoying our conversations, reach out, share your thoughts and journey with us. Don't forget to subscribe whilst you're here :)
---
Timestamps
00:00 Intro - The OG benchmarks - Turing test, MNIST, ImageNET
06:40 Are large language models benchmarks similar to humans taking tests?
10:05 Are we testing model capability vs production ready?
12:00 LLM era - data contamination
15:30 LM Arena - The leaderboard illusion paper - how big tech games benchmarks
28:35 Goodhart's law - When a measure becomes a target, it ceases to be a good measure
32:05 Some good benchmarks - games - Pokemon, ARC prize, Minecraft
34:35 Medical benchmarks - OpenAI's healthbench has some big problems
46:50 Microsoft AI-DXO orchestrator for case reports
---
Connect with Us
Your Hosts:
👨🏻‍⚕️ Doc - Dr. Joshua Au Yeung - LinkedIn
🤖 Dev - Zeljko Kraljevic - Twitter
Follow & Subscribe:
YT: https://youtube.com/@DevAndDoc
Spotify: Follow us on Spotify
Apple Podcasts: Listen on Apple Podcasts
Substack: https://aiforhealthcare.substack.com/
For enquiries:
📧 [email protected]
---
Production Credits
🎞️ Editor: Dragan Kraljević - Instagram
🎨 Brand & Art: Ana Grigorovici - Behance