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

AI agents are here, but how did we get here in the first place? How do we build and leverage AI agents for high stakes domains like healthcare? In this episode of Dev and Doc, we go deep into the forest that is AI agents and computer control - starting from the "caveman" era of LLMs discovering tools, to cultivating intelligent models and agentic workflows. We dissect everyday agents like MANUS AI, and deep dive into how, where and when AI agents should be used. Are these agents hype or hope, is this actually the second deepseek moment?

👋 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 :)

Episode Timestamps:
00:00 Highlight
3:13 start / intro
5:20 LLM's caveman era - tool usage
6:46 Agents have autonomy and interact with environment
11:15 workflows and agentic flows
15:30 when should you be using an agent?
24:27 vibe coding is like driving a car
29:07 Demo - MANUS gathering financial trends, computer control
35:55 Demo MANUS AI- website creation for Autism Assessment
49:05 computer control factions- Freedom vs Process automation
55:00 Autism website testing
59:13 summary + end

Hosts:
👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/
🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr

Find us on:
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

Claude sonnet 3.7 was released less than 48 hours ago, the model is highly intelligent and is one of the best we have seen in recent memory. Definitely passes the vibe check.

We give some amazing examples of coding with claude with few shot prompts, and cover technical and clinical evaluations and share our first thoughts. We even tested claude to take a patient history!

NB - PLEASE don't do this at home, obviously this is a demo and we do not in any way condone or recommend using an LLM as your doctor or healthcare provider, we are just demonstrating what the future could be. If you are sick, please seek a medical professional.

👋 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 start + highlights

01:54 Introduction

08:54 Benchmarks, state of the art

14:44 guardrails, refusals, AI safety and catastrophic risks

22:36 show and tell- great for coding and make video games!

26:54 example hospital runner

30:17 Medical use cases- clinical coding, biomedical entity extraction

37:04 only medical example in Claude model card- still hallucinating citations

38:37 making an anatomy app

40:10 forecasting clinical diagnoses

43:36 taking a medical history from a patient

53:33 wrap up

👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - linkedin.com/in/dr-joshua-auyeung

🤖Dev - Zeljko Kraljevic twitter.com/zeljkokr

YT:youtube.com/@DevAndDoc

Spotify:podcasters.spotify.com/pod/show/devanddoc

Apple:podcasts.apple.com/gb/podcast/dev-and-doc-ai-for-healthcare-podcast/id1751495120

Substack:aiforhealthcare.substack.com

For enquiries - 📧 [email protected]

🎞️ Editor - Dragan Kraljević instagram.com/dragan_kraljevic

🎨 Brand design - Ana Grigorovici behance.net/anagrigorovici027d

Is the academic system broken in this publish-or-perish landscape? When is a PhD not worth pursuing?

In this Dev and Doc episode, Zeljko (now associate professor!) and Josh (doctor, PhD drop out) talk about the good and the bad of PhD life. They provide insight into the academic world with a focus on computer science and machine learning.

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 - LinkedIn
• 🤖 Dev - Zeljko Kraljevic - Twitter

• 00:00 - Start and highlight
• 01:42 - Intro
• 03:11 - What made you pursue PhD in the first place
• 05:05 - Industry or PhD first
• 10:00 - Positives - Moonshots
• 17:03 - Positives - Access to world experts and collaboration
• 20:55 - Positives - Open source and open science
• 24:49 - Positives - A good environment enables a smooth PhD
• 27:04 - Negatives - You are a one-man show
• 31:33 - Negatives - Publish or Perish
• 45:44 - Bring your research closer to the audience through blogs and other media, journals are legacy media
• 51:20 - Verdict - Is a PhD still worth it in 2025?

• LinkedIn Newsletter
• YouTube
• Spotify
• Apple Podcasts
• Substack

For enquiries - [email protected]

• 🎬 Editor - Dragan Kraljević - Instagram
• 🎨 Brand Design & Art Direction - Ana Grigorovici - Behance

Dev and Doc put Deepseek R1 to the test in a technical and clinical deep dive.

👋 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

For enquiries - 📧[email protected]

Dev and Doc - Latest News

It's 2025, Dev and Doc cover the latest news including Google's deep research and notebook LM, DeepMind's Promptbreeder, and Anthropic's new RAG approach. We also go through what retrieval augmented generation (RAG) is, and how this technique is advancing LLM performance.

👋 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 :)

📧 For enquiries - [email protected]