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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 Highlight01:06 Ambient: Biggest game changer04:01 Open Source will catch up to closed source09:20 Big AI companies will fail10:52 There will be more trials involving large language models13:55 Industry will lead progress19:36 LLMs are not going to replace therapists or doctors22:17 AI psychosis and big tech27:19 People with AI replace people without AI29:12 Radiology AI will become more widespread31:00 Dev was way too optimistic about OpenAI; Google is coming for you33:05 Predictions we missed: GOOGLE KILLED EVERYONE37:10 Uprising of China Open source, xAI39:15 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 :) --- Timestamps00:00 Intro - The OG benchmarks - Turing test, MNIST, ImageNET06:40 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 contamination15:30 LM Arena - The leaderboard illusion paper - how big tech games benchmarks28:35 Goodhart's law - When a measure becomes a target, it ceases to be a good measure32:05 Some good benchmarks - games - Pokemon, ARC prize, Minecraft34:35 Medical benchmarks - OpenAI's healthbench has some big problems46:50 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/@DevAndDocSpotify: Follow us on SpotifyApple Podcasts: Listen on Apple PodcastsSubstack: 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

body{font-family:sans-serif;color:#fff;background:#121212;margin:0;padding:10px}p{margin:8px 0}h1{font-size:18px;margin:10px 0}.note{background:#535353;padding:10px;border-radius:4px;margin:10px 0}.timestamps span{color:#1DB954;font-weight:bold}a{color:#1DB954;text-decoration:none}Can Claude perform a range of complex clinical tasks? Dev and Doc are here to investigate.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 it still worth doing a PhD in 2025? Is the academic system broken in this publish-or-perish landscape? When is a PhD not worth pursuing? About this Episode 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. 👋 Connect With Us! 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 :) 🎙️ Hosts 👨🏻‍⚕️ Doc - Dr. Joshua Au Yeung - LinkedIn 🤖 Dev - Zeljko Kraljevic - Twitter ⏳ Timestamps 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? 📢 Follow Us LinkedIn Newsletter YouTube Spotify Apple Podcasts Substack 📧 Contact Us For enquiries - [email protected] 🎞️ Video Production 🎬 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 :) 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-au-yeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr TIMESTAMPS 00:00 Highlights 04:36 Intro 08:29 response from OpenAI, Anthropic- model training costs, tightening restrictions on China, pricing wars 13:13 what an open-source deepseek means for the world. 15:38 Sam altman and Dario amodei feeling the pressure 23:10 TECHNICAL deep dive - RLHF, ppo, dpo 37:08 GRPO, R1s secret sauce 45:02 the aha moment, learning like a human? 50:25 deepseek R1 training and controversy 59:08 deepseek healthcare evaluation - Ethnic Bias 1:06:17 The diagnostic acid test (fail) 1:12:46 Coding clinical data / Medical billing (shout out SNOMED) LinkedIn Newsletter https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7216474068085026817 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] 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d

Dev and Doc - Latest News 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 :) Meet the Team 👨🏻‍⚕️ Doc - Dr. Joshua Au Yeung - LinkedIn 🤖 Dev - Zeljko Kraljevic - Twitter Where to Follow Us LinkedIn Newsletter YouTube Spotify Apple Podcasts Substack Contact Us 📧 For enquiries - [email protected] Credits 🎞️ Editor - Dragan Kraljević - Instagram 🎨 Brand Design and Art Direction - Ana Grigorovici - Behance Episode Timeline 00:00 Highlights 00:53 News - Notebook LM, OpenAI 12 days of Christmas 07:44 Change in the meta - post-training 11:34 Optimizing prompts with DeepMind Promptbreeder 13:20 Is OpenAI losing their lead against Google 16:45 Deep research vs Perplexity 24:18 AIME and oncology 26:00 Deep research results 30:20 RAG intro 33:14 Second pass RAG 36:20 RAG didn't take off 38:40 Wikichat 39:16 How do we improve on RAG? 41:11 Semantic/topic chunking, cross-encoders, agentic RAG 51:15 Google’s Problem Decomposition 53:32 Anthropic’s Contextual Retrieval Processing 56:07 Summary and wrap up References Cross Encoders Wikichat Google's Problem Decomposition Anthropic's Contextual Retrieval Google AIME in Oncology DeepMind's Promptbreeder

First Thoughts and Preliminary Insights into OpenAI's GPT o1 Strawberry in the Medical Domain With some expected and unexpected findings, we have a "bake off" between o1 and Doc to demonstrate how o1 fares with tricky medical scenarios. Disclaimer Obviously, don't use AI to diagnose or treat your medical problems. If you are unwell, please seek a medical professional (AI isn't good enough just yet :)). 👋 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 :) Contributors • 👨🏻‍⚕️ Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ • 🤖 Dev - Zeljko Kraljevic - https://twitter.com/zeljkokr Follow Us • https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7216474068085026817 • https://youtube.com/@DevAndDoc • https://podcasters.spotify.com/pod/show/devanddoc • https://podcasts.apple.com/gb/podcast/dev-and-doc-ai-for-healthcare-podcast/id1751495120 • https://aiforhealthcare.substack.com/ For enquiries - 📧 mailto:[email protected] Team • 🎞️ Editor - Dragan Kraljević - https://www.instagram.com/dragan_kraljevic/ • 🎨 Brand Design and Art Direction - Ana Grigorovici - https://www.behance.net/anagrigorovici027d Timestamps • 00:00 - Start + Highlights • 01:28 - Intro, What is GPT o1? • 05:18 - What is "Reasoning" in o1? • 12:38 - Benchmarks: o1's Successes and Failures • 24:07 - o1 and Doctor Bake Off! • 24:21 - The Pregnancy Acid Test for LLMs • 26:23 - Clinical Coding • 30:06 - Tricky Patient Scenarios • 32:25 - Opioid Dose Conversions

Dev and Doc is joined by guest Annabelle Painter, doctor, CMO, and podcaster for the Royal Society of Medicine Digital Health Podcast. We deep dive into explainability and interpretability with concrete healthcare examples. Check out Dr. Painter's Podcast here, she has some amazing guests and great insights into AI in healthcare! - https://spotify.link/pzSgxmpD5yb 👋 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 LinkedIn Newsletter YouTube Channel Spotify Apple Podcasts Substack For enquiries - 📧 [email protected] 🎞️ Editor - Dragan Kraljević - https://www.instagram.com/dragan_kraljevic/ 🎨 Brand design and art direction - Ana Grigorovici - https://www.behance.net/anagrigorovici027d Timestamps: 00:00 - Start + highlights 03:47 - Intro 08:16 - Does all AI in healthcare need to be explainable? 15:56 - History and explanation of Explainable/Interpretable AI 20:43 - Gradient-based saliency and heat maps 24:14 - LIME - Local Interpretable Model-agnostic Explanations 30:09 - Nonsensical correlations - When explainability goes wrong 33:57 - Modern explainability - Anthropic 37:15 - Comparing LLMs with the human brain 40:02 - Clinician-AI interaction 47:11 - Where is this all going? Aligning models to ground truth and teaching them to say "I don't know" References: Fun Examples of when models go wrong - Nonsensical correlations Mechanistic interpretability Anthropic - Mapping the mind of language models Limitations of current AI explainability approaches Explainability does not improve automation bias in radiologists

An explainer on Foundation models for pathology, from Microsoft's Gigapath to Owkin's H-optimus-0, every company, big or small, are building pathology AI models. In this episode, Doc talks to Sean M. Hacking, assistant professor in Pathology at NYU Grossman School of Medicine and Özgür Şahin, particle physicist at CERN. Together they are building the infrastructure for digital pathology that then allows training of pathology foundational models. Find out more at https://www.pathonn.com/. 👋 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 :) https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7216474068085026817 https://youtube.com/@DevAndDoc https://podcasters.spotify.com/pod/show/devanddoc https://podcasts.apple.com/gb/podcast/dev-and-doc-ai-for-healthcare-podcast/id1751495120 https://aiforhealthcare.substack.com/ 👨🏻‍⚕️Doc - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - https://twitter.com/zeljkokr 🎞️ Editor - https://www.instagram.com/dragan_kraljevic/ 🎨 Brand design and art direction - https://www.behance.net/anagrigorovici027d 00:00 Introduction 03:28 Why pathology 06:42 Transporting slides is a logistical nightmare 13:20 When particle physics and AI pathology collide 17:55 AI digital pathology - Patch-based architecture and sparse topologies 27:09 Is there enough pathology data? 29:11 Microsoft and Gigapath, transformer models for pathology 33:55 Pathology models clinical applications 43:18 Staining applications of AI 49:22 Building a digital pathology startup - Patho-NN 57:36 Using AI to see tumor grading features that humans can’t see References: https://www.nature.com/articles/s41586-024-07441-w https://www.microsoft.com/en-us/research/blog/gigapath-whole-slide-foundation-model-for-digital-pathology/ https://www.nature.com/articles/s41379-021-00919-2

Doc talks to Dr Derrick Khor - Cancer Doctor, HealthTech Consultant and Linkedin Guru. We share Derrick's insights from consulting over 120 companies and a step-by-step guide on how to build a successful Healthcare company. You can find more of Derrick and his helpful guides - https://adoptadoc.com/resources/ profile- https://www.linkedin.com/in/derrick-khor/ 👋 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 :) Dev&Doc is a podcast where doctors and developers deep dive into the potential of AI in healthcare. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung 🤖Dev - Zeljko KraljevicLinkedIn NewsletterYouTubeSpotifyAppleSubstack For enquiries - 📧 [email protected] 🎞️ Editor - Dragan Kraljević 🎨 Brand design and art direction - Ana Grigorovici Timestamps 00:00 Highlights and intro 3:01 Start 5:10 getting into health tech 8:03 lack of clinicians in start ups 15:07 Derrick's own healthtech journey to consulting 23:37 Start ups and failure 27:35 the start up road map 32:16 are you a medical device (samd)? Intended use 40:55 clinical evidence generation 48:16 go to market, NHS DTAC 57:57 power of networking, social media, linkedin 1:02:43 top UK health tech companies to look out for

Dev and Doc deconstruct digital biomarkers! This is a fascinating and nascent field in the world of medicine, how have biomarkers transformed the way we practice medicine, and how will AI and wearables, sensors and digital fingerprints transform the way we practice in the future? 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 :) find us on youtube- @Dev and Doc 📙Substack: https://aiforhealthcare.substack.com/👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d Timestamp 00:00 highlights 01:50 intro 02:40 how biomarkers evolved in the last century 6:02 what is the definition of a biomarker 10:00 biomarkers can be very biased depending on who you are testing 12:31 when does a test become a biomarker 17:30 the digital age and measurements - AI vision in retina scans, digital stethoscopes 23:50 what is an “analog” biomarker vs digital biomarker? 30:10 where do biomarkers fail in evidence based medicine? 34:55 Biomarkers are pretty poor for mental health 47:57 can AI predict depression better than humans? 51:21 Digital biomarkers to detect movement disorders 01:00:04 this can change clinical trials forever Refs - variable definitions of biomarkers https://informatics.bmj.com/content/31/1/e100914 -digital biomarkers convergence nature paper https://www.nature.com/articles/s41746-022-00583-z -digital stethoscope for heart failure https://www.thelancet.com/pdfs/journals/landig/PIIS2589-7500(21)00256-9.pdf -touch screen typing depression paper https://www.nature.com/articles/s41746-022-00583-z - Duchennes body suit biomarker https://www.nature.com/articles/s41591-022-02045-1#Sec9 - Friedreichs ataxia body suit https://www.nature.com/articles/s41591-022-02159-6?fromPaywallRec=false#Sec9