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 - 📧 Devanddoc@gmail.com

🎞️ 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

Dev And Doc are back ! Here we break down the biggest highlights of 2023, and AI predictions for 2024. Dev and Doc is a Podcast where developers and doctors join forces to deep dive into AI in healthcare. Together, we can build models that matter. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr 00:00 start 01:01 Intro, Advancing LLMs in healthcare 07:10 Ambient note documentation in Medicine 10:52 Meta LLaMa are the good guys ? 14:40 GPT store 19:40 Overhyped Google Gemini model 26:17 AGI again 29:05 6 big predictions Open source vs Closed source models 38:55 AI in healthcare- LLM clinical trials , AI drug discovery 42:05 end References GPT store- https://openai.com/blog/introducing-the-gpt-store Hugging face predictions- https://twitter.com/ClementDelangue/status/1729158744762626310 AI drug discovery (blog post to paper) - https://news.mit.edu/2023/using-ai-mit-researchers-identify-antibiotic-candidates-1220 Google AMIE blog - https://blog.research.google/2024/01/amie-research-ai-system-for-diagnostic_12.html The podcast 🎙️ 🔊Spotify: https://open.spotify.com/show/3QO5Lr3w4Rd6lqwlfKDaB7?si=e7915d844994403e 📙Substack: https://aiforhealthcare.substack.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 :) 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d

We have conversations between doctors and developers exploring the potential of AI in healthcare Josh is a training Neurologist in the NHS, and AI researcher in St Thomas' hospital and King's College Hospital. He is also a PhD student at King's College London. Zeljko is an AI researcher and PhD student at King's College London, as well as a CTO for a natural language processing company.

In this episode, Dev and Doc sit down to discuss artificial general intelligence from the perspective of a neurologist and computer scientist. We dive into the current developments around AGI , the 2 controversial schools of thought, LLMs and neuroscience, and give hot takes about whether we will ever reach AGI. Dev and Doc is a Podcast where developers and doctors join forces to deep dive into AI in healthcare. Together, we can build models that matter. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr 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 :) 00:00 start 01:05 intro 03:46 two camps of AGI - Yann Lecun vs Geoffrey Hinton, Architecture vs Data 07:47 Do emergent capabilities of LLMs pose a threat to humanity? 08:45 Intelligence and AGI - neuroscience and computer science approach 16:59 LLMs vs the human brain 24:16 Do AIs need a human touch? - Intrinsic personalities, temperaments, motivations, joy and reward The podcast 🎙️ 🔊Spotify: https://open.spotify.com/show/3QO5Lr3w4Rd6lqwlfKDaB7?si=e7915d844994403e 📙Substack: https://aiforhealthcare.substack.com/ 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d

De-identifying and anonymising PHI (protected/personal health information) in health records is one of the central pillars of AI success in healthcare. Without de-identified data we cannot share data between hospitals , train models confidentially, or safely create large language models. Live from new orleans, Dev and Doc are here to dive into this fascinating topic, as well as describe our experiences of building and deploying an AI model with over 99% recall for redaction of PHI. Dev and Doc is a Podcast where developers and doctors join forces to deep dive into AI in healthcare. Together, we can build models that matter. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua... 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr 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 :) 00:00 start 00:52 intro 2:10 what is PHI? Personal /private health information 7:00 approaches on de-identifying hospital records 9:55 the problem with over-redaction /anonymisation 11:33 using deep learning for anonymisation 14:13 our experiences building a over 99% recall model VS manual annotation 18:03 how to make a high performing model - the art of annotations 24:49 Dev and Docs annotation method (Zeljko et al.) 30:42 how do you prevent overfitting? 31:54 ensuring model performs in new hospital / environments 33:23 future 34:48 synthetic data The podcast 🎙️ 🔊Spotify: https://open.spotify.com/show/3QO5Lr3... 📙Substack: https://aiforhealthcare.substack.com/ 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kral... 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovic...

What is the current state of healthcare in the UK, is it ethical for patients to use LLMs for their healthcare ? Dev and Doc is a Podcast where developers and doctors join forces to deep dive into AI in healthcare. Together, we can build models that matter. 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 00:25 Intro 01:25 A personal account of the current state of healthcare 05:18 Did the covid pandemic really make a difference? 08:30 The NHS elective waiting list - 7.7 million patients and counting 12:04 Ghost patients - a data conundrum 13:55 BBC hospital waiting time tracker, ambulance wait times 19:00 Using AI / NLP to tackle elective lists 19:50 Patients will use tech like LLMs and ChatGPT to self diagnose 22:00 Symptoms need to be explored 26:10 data distribution on the web is skewed 28:50 Is it permissible to use LLMs in healthcare? 32:35 Directions for the future 35:20 ending The podcast 🎙️ 🔊Spotify: https://open.spotify.com/show/3QO5Lr3w4Rd6lqwlfKDaB7?si=e7915d844994403e 📙Substack: https://aiforhealthcare.substack.com/ 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d Refs BBC wait tracker - https://www.bbc.co.uk/news/health-59549800 NHS waiting list, health foundation- https://www.health.org.uk/waiting-list

As the AI safety summit nears in England, the UK positions itself as a leader in AI safety, but what does this mean? Is AI safety all about preventing doom for the human race ? 🤖Dev and doc👨🏻‍⚕️ are here to break down this fascinating topic. Dev and Doc is a Podcast where developers and doctors join forces to deep dive into AI in healthcare. Together, we can build models that matter. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr 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 :) 00:00 intro 01:28 Start 06:30 AI safety definition 10:21 AI safety vs AI regulation 13:05 UK positions itself as a leader in the AI safety summit (featuring Rishi Sunak) 15:50 AI safety summit - responsible scaling 16:50 what does this mean for an AI researcher? When should we slow down research? 19:40 Yann Lecunn- we will get nowhere by simply scaling. The transformer architecture will NOT lead to AGI 25:30 Tackling AI safety with model evaluation,red teams and ethical hackers 33:36 Trusts sharing data, federated learning platform, over-regulation 38:57 google already has all of your data 41:49 there is a lack of research on AI safety The podcast 🎙️ 🔊Spotify: https://open.spotify.com/show/3QO5Lr3w4Rd6lqwlfKDaB7?si=e7915d844994403e 📙Substack: https://aiforhealthcare.substack.com/ 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d

🤖Dev and doc👨🏻‍⚕️ introduces large multimodal models. ✨ The potential of LMMs combining text and images seem limitless, but what's the catch? Dev and Doc is a Podcast where developers and doctors join forces to deep dive into AI in healthcare. Together, we can build models that matter. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr 00:00 start 00:32 intro 02:20 what is multimodality? And what are the potentials? 09:43 Large multimodal models paper deep dive (radiology) 18:43 paper deep dive 2 (pathology) 20:40 large multimodal models technical overview, exploration of other LMMs 31:40 Foundational models explanation 35:18 the model transparency index 36:20 Google PaLI-3, light weight models vs large Foundational models 43:04 Summary 44:15 the problems and work to be done for LMMs - hallucinations, inconsistencies, biases, security 49:20 A call for better evidence generation and trials with LMMs 53:00 final points - improving visual spatial recognition, thoughts for future The podcast 🎙️ 🔊Spotify: https://open.spotify.com/show/3QO5Lr3w4Rd6lqwlfKDaB7?si=e7915d844994403e 📙Substack: https://aiforhealthcare.substack.com/ 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d

How should one get into AI and Health tech? Here Doc offers the top 5 tips. Are you someone who is looking into AI and health tech? Or someone who is already in the field ? Share your thoughts and journey with us! Dev and Doc is a Podcast where developers and doctors join forces to deep dive into AI in healthcare. Together, we can build models that matter. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr 00:00 start 00:25 intro 02:05 why do developers not want to get into healthcare (shock) 05:45 what does Dev and Doc love about healthcare 09:20 AI is a multiplicator ------- 5 tips to get into health tech / AI----- 10:32 how a doctor can get into AI 10:40 1.AI is a collection of many topics and skills 13:23 should I learn to code ? 15:25 2.Understanding your intention 26:20 3.find a project 28:30 4.Find a team 31:54 5.Perseverance References: - https://discord.com/invite/hugging-face-879548962464493619 [Huggingface] - https://discord.com/invite/Mw77HPrgjF [Chipro] - https://www.reddit.com/r/learnmachinelearning/ - https://www.datacamp.com/ - https://www.kaggle.com/ - https://www.coursera.org/ - https://www.youtube.com/@AndrejKarpathy

We start with a deep dive into why AI is required for the future of healthcare, and in what ways can AI be integrated. In the second part of the episode, we cover the VC involvement in the space and showcase the best use cases where AI companies can jump into healthcare. <p>Dev&Doc is a podcast where doctors and developers deep dive into the potential of AI in healthcare.<br>

👨🏻‍⚕️Doc - <a href="https://www.linkedin.com/in/dr-joshua-auyeung/">Dr. Joshua Au Yeung</a><br>

🤖Dev - <a href="https://twitter.com/zeljkokr">Zeljko Kraljevic</a><br>

<a href="https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7216474068085026817">LinkedIn Newsletter</a><br>

<a href="https://youtube.com/@DevAndDoc">YouTube</a><br>

<a href="https://podcasters.spotify.com/pod/show/devanddoc">Spotify</a><br>

<a href="https://podcasts.apple.com/gb/podcast/dev-and-doc-ai-for-healthcare-podcast/id1751495120">Apple</a><br>

<a href="https://aiforhealthcare.substack.com/">Substack</a><br>

For enquiries - 📧 <a href="mailto:Devanddoc@gmail.com">Devanddoc@gmail.com</a>

</p>

Timestamps: 00:00 Start 00:20 Intro - Does AI need healthcare? 01:45 Definitions of AI 06:25 the current state of healthcare needs intervention 08:14 are modern doctors spending more time with patients? 10:00 what can AI do for clinicians 15:59 AI super doctors! 22:09 will AI take over in our lifetimes? 23:38 industry paying attention, FDA, media, venture capital and businesses 26:20 barriers to patients getting right care Report segment - trying to bridge the gap from research to health tech companies /businesses 29:39 venture capital a16z report - "AI jobs to be done" 32:00 an ethical dilemma. When no doctor is available, Is it better to have a medical AI than no one? 37:12 low hanging AI fruit - Clinical coding /billing 41:55 ai driven talk therapy 45:20 Psychology, Psychiatry, Neurology, segregated professions that should unite 47:10 other cases - scheduling patient appointments 51:20 ending References: https://www.sciencedirect.com/science/article/abs/pii/S0160791X23001264 https://www.bloomberg.com/news/articles/2023-09-01/tech-investors-bet-ai-finally-poised-to-transform-health-care?leadSource=uverify%20wall https://arxiv.org/pdf/2306.02022.pdf https://arxiv.org/pdf/2309.07430.pdf

<p>What does it mean for a doctor to diagnose a patient? Can an AI (GPT model) learn to diagnose a patient, and even out-diagnose a doctor? Can we use AI to predict the future?</p>

<p>Try the Foresight GPT demo here: <a href="https://foresight.sites.er.kcl.ac.uk/">foresight.sites.er.kcl.ac.uk</a></p>

<p>Like what you're hearing? Support us by subscribing and reaching out to us. We want to encourage open discussion between clinicians and developers.</p>

<p>Dev&Doc is a podcast where doctors and developers deep dive into the potential of AI in healthcare.<br>

👨🏻‍⚕️Doc - <a href="https://www.linkedin.com/in/dr-joshua-auyeung/">Dr. Joshua Au Yeung</a><br>

🤖Dev - <a href="https://twitter.com/zeljkokr">Zeljko Kraljevic</a><br>

<a href="https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7216474068085026817">LinkedIn Newsletter</a><br>

<a href="https://youtube.com/@DevAndDoc">YouTube</a><br>

<a href="https://podcasters.spotify.com/pod/show/devanddoc">Spotify</a><br>

<a href="https://podcasts.apple.com/gb/podcast/dev-and-doc-ai-for-healthcare-podcast/id1751495120">Apple</a><br>

<a href="https://aiforhealthcare.substack.com/">Substack</a><br>

For enquiries - 📧 <a href="mailto:Devanddoc@gmail.com">Devanddoc@gmail.com</a>

</p>

Timestamps: 00:00 Start 00:49 Intro 02:18 How does a doctor diagnose? 06:27 LLM trained on medical school vs real clinical scenarios 10:54 Do you have time to see every patient (in detail)? 13:44 A doctor's day 22:28 Which doctor's jobs can be automated? 27:41 When you train models on the wrong goal (misalignment) 31:01 Low hanging fruit - GPT / LLM text summarisation , administrative tasks 37:25 What tools are helping doctors right now? 40:35 How does a doctor prognosticate? How are risk scores created? 49:05 Foresight- Using AI to Predict the Future 58:31 GPT graduates from medical school. GPT starts working in a hospital 1:02:25 Why are big tech not building the next real-world medical LLM? 1:04:44 Using AI to predict the future. Foresight GPT demo 1:10:18 predict diagnosis respiratory infections, COVID-19 1:11:40 predicting obstructive sleep apnoea 1:14:12 predicting polycystic kidney disease 1:18:01 wrap up

<p>🎞️ Editor - <a href="https://www.instagram.com/dragan_kraljevic/">Dragan Kraljević</a></p>

<p>🎨 Brand design and art direction - <a href="https://www.behance.net/anagrigorovici027d">Ana Grigorovici</a></p>

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 Kraljevic
LinkedIn Newsletter
YouTube
Spotify
Apple
Substack
For enquiries - 📧 Devanddoc@gmail.com

<p>🎞️ Editor - <a href="https://www.instagram.com/dragan_kraljevic/">Dragan Kraljević</a></p>

<p>🎨 Brand design and art direction - <a href="https://www.behance.net/anagrigorovici027d">Ana Grigorovici</a></p>

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

In this episode, we introduce large language models in healthcare, their potentials and pitfalls. We put AI chatbots like ChatGPT to the test, discuss our thoughts on Google's Med-PaLM, and dabble in a bit of philosophy of artificial general intelligence.

Like what you're hearing? Support us by subscribing and reaching out to us. We want to encourage open discussion between clinicians and developers.

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 Kraljevic
LinkedIn Newsletter
YouTube
Spotify
Apple
Substack
For enquiries - 📧 Devanddoc@gmail.com

Timestamps:
00:00 Start
00:16 Intro
02:04 ChatGPT, A giant leap for mankind?
04:02 Spending two weeks with ChatGPT as a doctor
07:36 History of Large Language Models (LLMs)
10:15 A top down approach to what is an LLM
17:31 Medical language is a language in itself
18:42 A lot of data, that is just wrong
21:05 Self-supervised training LLM
22:05 Instruction based fine tuning a LLM
23:52 Doc summarizing LLM training
25:48 The clinical shortcomings of instruction based tuning
27:33 Reinforcement learning from Human (clinician) feedback
32:22 Doc summarizing LLM, RLHF - A strict vs a progressive parent
34:10 There are still many problems with LLMs, aligning with clinical training data
36:26 Training a LLM on discharge summaries is a bad idea
39:18 Garbage in garbage out - data
40:13 Context windows
40:43 Data cleaning clinical notes
44:31 Bias in scientific domain LLMs PubmedGPT, Galatica
46:31 Data drift in medicine and continual learning
50:01 MedPaLM - instruction tuning to the medical domain
50:23 Model benchmarks do not reflect the real world
59:11 LLM emulating human language, but not the brain. Only one piece of the mind
1:01:10 LLMs on headaches and general knowledge
1:03:25 Where does a LLM fit in into the clinical work flow
1:05:50 Are regulations working against safety?
1:08:26 Cooling down LLMs to pass regulations
1:09:50 Why call it a hallucination? It's a false positive
1:13:57 Examples of bias of ChatGPT - A bad Santa Claus
1:17:00 Do LLMs encode true "understanding"? Can language lead to AGI?
1:20:05 Pregnancy - an acid test for Large Language Models
1:21:55 Training a LLM for the NHS (NHS-LLM)
1:25:25 Tell the model to "think deeply"
1:27:55 Asking ChatGPT to draw a picture of a human O.O
1:31:00 Language is not enough to achieve AGI
1:32:10 What can clinicians do about LLMs? Assisting vs Autonomous
1:39:07 What's next- Forecasting diagnoses with AI

🎞️ Editor - Dragan Kraljević
🎨 Brand design and art direction - Ana Grigorovici

In this episode we explore named entity recognition (NER) and its uses in clustering 1 million hospital inpatients, monitoring pandemics and outbreaks, automating clinical coding, enriching research cohorts, and more.

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 Kraljevic
LinkedIn Newsletter
YouTube
Spotify
Apple
Substack
For enquiries - 📧 Devanddoc@gmail.com

Timestamps:
00:00 Start
00:38 Intro
01:03 Setting the scene, clinical audit
03:13 What is Named Entity Recognition (NER)
14:59 Medical text as its own language
16:43 Medical abbreviations test
19:23 NER in different industries
21:55 NER with neural networks, deep learning, large language models
24:25 MedCAT medical concept annotation tool
25:50 When AI models go wrong, women get erectile dysfunction
28:20 Teaching a model to disambiguate
31:12 NER use case 1 - Clinical audit
33:04 How to fine tune a clinical model with clinician knowledge
36:07 NER use case 2 - Automating clinical audits
37:13 Why is NER not being used in the NHS? Windows XP
40:19 NHS is resistant to change
42:15 NER use case 3 - Enriching research databases
44:57 Which model should I use?
47:02 NER use case 4 - Extracting diseases from 1 million patients in King's College Hospital
52:05 Clustering 1 million patients with AI
55:14 Top 10 diagnoses in South London
58:45 Diseases by age in MIMIC dataset
1:01:27 Monitoring pandemic outbreaks
1:04:30 Predicting the future with Foresight

References:
Using machine learning for automated auditing of stroke comorbidities
Hospital-wide natural language processing summarising the health data of 1 million patients

🎞️ Editor - Dragan Kraljević
🎨 Brand design and art direction - Ana Grigorovici

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 Kraljevic The podcast 🎙️ 🔊Spotify- https://sptfy.com/OMmV Join the Dev&Doc movement: 🏃 https://aiforhealthcare.substack.com/ Timestamps: Start 00:00 Introduction 00:28 The idea 01:20 Target audience 2:46 Start of the AI journey 04:15 Medical school and Computer science degrees 05:25 Background of Dev and Doc 05:51 Dev goes into healthcare 07:17 Doc goes into AI 09:20 Neurology, a second love 12:22 What to expect, NLP, large language models, NER, generative AI 13:16 The main goal of the podcast 16:11 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici Reach out - 📧Devanddoc@gmail.com LinkedIn - https://shorturl.at/esEKO

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

Dr Keith Grimes is a HealthTech consultant and General Practitioner working with companies to transform clinical ideas into something impactful. He worked as the digital health director in Babylon Health prior to its demise, and currently runs his own consulting firm, Curistica. This is one not to miss! References HealthTech consulting at Curistica www.curistica.com Prof Amanda Goodall on leadership theory https://amandagoodall.com/ For those interested in Leadership opportunities: -Faculty of medical leadership and management https://www.fmlm.ac.uk/ -Bite labs https://www.bitelabs.io/ <p>Dev&Doc is a podcast where doctors and developers deep dive into the potential of AI in healthcare.<br>

👨🏻‍⚕️Doc - <a href="https://www.linkedin.com/in/dr-joshua-auyeung/">Dr. Joshua Au Yeung</a><br>

🤖Dev - <a href="https://twitter.com/zeljkokr">Zeljko Kraljevic</a><br>

<a href="https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7216474068085026817">LinkedIn Newsletter</a><br>

<a href="https://youtube.com/@DevAndDoc">YouTube</a><br>

<a href="https://podcasters.spotify.com/pod/show/devanddoc">Spotify</a><br>

<a href="https://podcasts.apple.com/gb/podcast/dev-and-doc-ai-for-healthcare-podcast/id1751495120">Apple</a><br>

<a href="https://aiforhealthcare.substack.com/">Substack</a><br>

For enquiries - 📧 <a href="mailto:Devanddoc@gmail.com">Devanddoc@gmail.com</a>

</p>

🎞️ 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 1:10 Career career career - GP, babylon health, digital consultancy 6:40 working as a rural GP in Scotland 9:21 time is the biggest factor of clinical impact 12:11 finding impact through data  21:29 leading by example  23:52 Should doctors be leading healthtech businesses?  30:10 why do healthtech start-ups not have clinicians earlier?  36:30 Babylon failure - importance of having clinical influence at the top  43:55 experience being grilled on BBC newsnight  49:45 lessons learnt from the downfall of Babylon  52:25 6 values of consulting firm Curistica  55:51 common problems in start ups  59:36 how AI will change the healthcare landscape

How do we reach the holy grail of a clinically safe LLM for healthcare? Dev and Doc are back to discuss news with Meta's LlaMA model and potential of healthcare LLMs finetuned on top like BioLlaMa. We discuss the key steps in building a clinically safe LLM for healthcare for healthcare and how this was pursued by Hippocratic AI's latest model - Polaris. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr The podcast 🎙️ 🔊Spotify: https://podcasters.spotify.com/pod/show/devanddoc 📙Substack: https://aiforhealthcare.substack.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 :) 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d References Hippocratic AI LLM- https://arxiv.org/pdf/2403.13313 BioLLM tweet - https://twitter.com/aadityaura/status/1783662626901528803 Foresight lancet paper -https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00025-6/fulltext Linear processing units- https://wow.groq.com/lpu-inference-engine/ Timestamps 00:00 Start 01:10 Intro- llama3 , a chatGPT level model in our hands 06:53 Linear processing units to run LLMs 09:42 BioLLM for medical question and answering 11:13 quality and size of dataset, using youtube transcripts 12:41 Question and answering pairs do not reflect the real world - holy grail of healthcare llm 18:43 Dev has Beef with hippocratic AI 20:25 Step1 Training a clinical foundational model from scratch 22:43 Step 2 Instruction tuning with multi-turn simulated conversation 24:15 Step 3 training the model to guide model in tangential conversations 27:42 Focusing on the hospital back office and specialist nurse phone calls 33:02 Evaluating Polaris - clinical safety LLM , bedside manner, medical safety advice

In this special episode we share a live recording of our live podcast episode at the Rewired UK conference, where NHS, industry and policy markers unite. We discuss current LLMs from a technical and practical perspective. Dive into how to build Foundational models for the National health service and our experiences. We were also privileged to be joined by head of digital at Cambridge University Hospital NHS trust, Dr. Wai Keong Wong on how to evaluate AI products and discussions on automating administrative tasks for clinicians with ambient clinical documentation. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr The podcast 🎙️ 🔊Spotify: https://podcasters.spotify.com/pod/show/devanddoc 📙Substack: https://aiforhealthcare.substack.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 :) 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d 00:00 intro 02:05 AI vs doctors - are language models ready to replace doctors? 05:22 the tranformer models and attention 08:51 human labour for reinforcement learning 11:00 building the NHS LLM, key concepts 13:55 foresight GPT - predicting the next clinical event in a patient timeline. 16:29 is text enough? 17:19 £3.8B investment into NHS digitisation and admin automation - ambient clinical documentation 20:14 how do you evaluate AI products for the NHS? 26:24 how do you vet the tech companies and future proof your purchase? 27:23 do clinicians need more digital health education? 28:41 transparency of AI models and benchmarks 31:30 question - EHR data created by AI leads to homogenisation and errors 34:03 question - training on structured vs unstructured EHR data 38:06 question - LLMs as a brain. How do we give it a body? 41:05 framework for ai deployment

What do Prompt engineers have in common with telephone operators in the 1870s? Spoiler - they're both dying professions 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr The podcast 🎙️ 🔊Spotify: https://podcasters.spotify.com/pod/show/devanddoc 📙Substack: https://aiforhealthcare.substack.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 :) 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d 00:00 Highlights 01:10 Intro - where did prompt engineering go wrong? 4:10 what is prompt engineering fundamentally? 10:54 LLMs training data reflects prompt engineering 12:32 prompts are model dependent 14:02 prompts that make you think 18:26 combining expert and generalist medical models for doctors 19:49 Diagnostic reasoning prompts, is it interpretable? 26:55 can we find prompts more elegantly/ systematically ? 28:42 Will prompts become obsolete? Models that self discover prompts 31:09 Telephone operators and Prompt engineers - death of a profession Refs Prompt "hacks" (oh man) - https://learnprompting.org/docs/intermediate/chain_of_thought Diagnostic prompt interpretability paper - https://www.nature.com/articles/s41746-024-01010-1 self - discover https://arxiv.org/abs/2402.03620 telephone operators - https://www.history.com/news/rise-fall-telephone-switchboard-operators

How do we align AI models for healthcare? 👨‍⚕️ And importantly, the moral codes and ethics that we practice everyday, how does the LLM deal with ethical scenarios like the trolley problem for example? This is a fascinating topic and one we spend a lot of time thinking about. In this episode Dev and Doc, Zeljko Kraljevic and I cover all the up to date topics around reinforcement learning, the benefits and where it can go wrong. We also discuss different RL methods including the algorithms used to train ChatGPT (RLHF). Dev and Doc is a Podcast where developers and doctors join forces to deep dive into AI in healthcare. Together, we can build models that matter. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua... 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr The podcast 🎙️ 🔊Spotify: https://open.spotify.com/show/3QO5Lr3... 📙Substack: https://aiforhealthcare.substack.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 :) 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kral... 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovic...00:00 Highlights 01:27 start 4:38 aligning ethics of ai models 7:04 doctors ethical choices daily 8:00 RLHF and AI training methods 16:29 reinforcement learning 19:35 Preference model -rewarding models correctly can make or break the success 27:05 exploiting reward function, model degradation (and how to fix it) Ref AI intro paper - https://pn.bmj.com/content/23/6/476 Open AI RLHF paper - https://arxiv.org/abs/1909.08593 War and peace of LLMs! - https://arxiv.org/abs/2311.17227

In this episode Doc goes on an adventure to chair an LLM/ generative AI conference session and reflects on his experience. Dev and Doc also discuss big news on meta's Llama3 and Code LlaMa. Dev and Doc is a Podcast where developers and doctors join forces to deep dive into AI in healthcare. Together, we can build models that matter. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/ 🤖Dev - Zeljko Kraljevic https://twitter.com/zeljkokr The podcast 🎙️ 🔊Spotify: https://open.spotify.com/show/3QO5Lr3w4Rd6lqwlfKDaB7?si=e7915d844994403e 📙Substack: https://aiforhealthcare.substack.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 :) 🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/ 🎨Brand design and art direction - Ana Grigorovici https://www.behance.net/anagrigorovici027d 00:00 Highlight 00:36 Start 1:57 Are researchers just using Generative AI to get presentations /publications? 6:18 Hype cycles , lack of real world clinical studies using LLMs 8:08 LlaMa3 , Code LlaMa announcement and insights 13:30 Google bard / Gemini ultra second on leaderboard 17:30 wrap up and end

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:Devanddoc@gmail.com 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 - 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 - Devanddoc@gmail.com

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

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 - 📧Devanddoc@gmail.com

🎞️ Editor- Dragan Kraljević https://www.instagram.com/dragan_kraljevic/
🎨Brand design and art direction - Ana Grigorovici https://www.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 - devanddoc@gmail.com

🎞️ Video Production

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

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 - 📧 Devanddoc@gmail.com

🎞️ Editor - Dragan Kraljević instagram.com/dragan_kraljevic

🎨 Brand design - Ana Grigorovici behance.net/anagrigorovici027d

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:
📧Devanddoc@gmail.com

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:
📧 Devanddoc@gmail.com

---

Production Credits
🎞️ Editor: Dragan Kraljević - Instagram
🎨 Brand & Art: 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:
📧 Devanddoc@gmail.com

Credits:
🎞️ Editor - Dragan Kraljević - https://www.instagram.com/dragan_kraljevic/
🎨 Brand design and art direction - Ana Grigorovici - https://www.behance.net/anagrigorovici027d

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: Devanddoc@gmail.com
🎞️ 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: Devanddoc@gmail.com

Credits:
🎞️ Editor: Dragan Kraljević (Instagram)
🎨 Brand Design: Ana Grigorovici (Behance)

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: 📧 Devanddoc@gmail.com

— Credits —
🎞️ Editor: Dragan Kraljević - Instagram
🎨 Brand Design: Ana Grigorovici - Behance

On this episode of Dev and Doc, Doc sits down with Dr Hamilton Morrin, a psychiatrist and doctoral fellow exploring the intersection of AI and psychiatry. Together, we have seen first hand the impact and rise of AI psychosis, and over time we have been mapping out and publishing frontier research on this topic.

Here we share everything you need to know about the clinical and technical aspects of AI psychosis, and its downstream impacts on society and medicine.

👋 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 Introduction, following AI psychosis over time
07:29 Themes of AI psychosis / AI-associated delusions
14:50 What even is psychosis in mental health context?
23:58 Tracking cases of AI psychosis
40:27 Is AI psychosis just another wave of technological harm? Or is there a technological difference?
47:53 Misalignment between what companies vs engagement
54:45 Psychosis bench- benchmarking AI Psychosis propensity in LLMs
1:04:40 What can we do about it?

To support us:
buymeacoffee.com/devanddoc

👨🏻‍⚕️Doc - Dr. Joshua Au Yeung - LinkedIn
🤖Dev - Zeljko Kraljevic - Twitter/X

Follow us:
YT - YouTube
Spotify - Spotify
Apple - Apple Podcasts
Substack - Substack

For enquiries: 📧 Devanddoc@gmail.com

🎞️ Editor - Dragan Kraljević - Instagram
🎨 Brand design and art direction - Ana Grigorovici - Behance