Here’s a quick wrap of the three papers we found interesting over the last few weeks with some take home points.

More details in the show notes on our website.

Episodes | Bluesky | [email protected]

We’re trying out a new episode format! We’ll be doing a quick wrap of the top three papers we found interesting over the last few weeks with some take home points.

More details in the show notes on our website.

Episodes | Bluesky | [email protected]

In this episode, we tackle some common myths about how generative AI works, why this is the case, implications for healthcare and some quick fixes. These myths include 1) that LLMs can explain their reasoning 2) that LLMs can express uncertainty, 3) that LLMs can a) do maths, b) manage temporal data c) apply guidelines d) handle negation and finally that 4) that AI will replace clinicians.

02:00 Technical update - DeepSeek, other new models

10:00 - AI mybusting

15:50 - LLMs can explain their reasoning

21:50 - LLMs can express uncertainty

26:40 - LLM blindspots

41:50 - AI will replace clinicians

Episodes | Bluesky | [email protected]

In this episode, we discuss algorithmic bias and fairness in healthcare. We explain what this is, the different definitions of “fairness”, explore the ways in which bias can enter the machine learning pipeline and some ways to combat it.

01:00 Technical update - NeurIPS

06:50 Technical update - ChatGPT-o3

14:00 - Algorithmic bias

Episodes | Bluesky | [email protected]

We’ve returned after an accidental hiatus, just in time for the end of the year. In this episode, we’re joined by the team behind the American Medical Informatics Association (AMIA) Year in Review - Professor James Cimino, Pushkala Jayaraman and Dr Humayera Islam to talk about the main themes in healthcare AI for 2024.

01:00 Technical update (ChatGPT-o1, agentic AI)

Episodes | Bluesky | [email protected]

We’re back! This is the start of our regular discussions about healthcare AI topics and recent literature. On today’s episode -  the 10 commandments of decision support, the checkered history of EMRs, clinicians as “moral crumple zone” for AI models and much more.

01:18 Technical update (Llama 3.1, Phi releases)

06:25 Main discussion

41:20 Article round-up

Episodes | Twitter | [email protected]  

In this episode, we discuss more of the technical aspects of LLM implementation in healthcare, including the following topics:

• Embeddings

Oh my!

Episodes | Twitter | [email protected]  

In this episode, we discuss some concerns about LLM implementation in the healthcare setting, including the following topics:

• More detail about hallucinations, issues with accuracy 

Episodes | Twitter | [email protected]  

Finally! We’re talking about large language models (LLMs) including ChatGPT. We discuss the following topics:

• Brief explanation of transformer models and how they work including the attention mechanism and context window

Episodes | Twitter | [email protected]  

In this episodes we give an overview of neural networks and how they’re used in healthcare. We'll be covering the following topics:

• Definition of neural networks and a high level explanation of their structure

Episodes | Twitter | [email protected]