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

00:35 - Scaling Large Language Models for Next-Generation Single-Cell Analysis

05:55 - Generative Medical Event Models Improve with Scale

10:50 - When helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior

More details in the show notes on our website.

Episodes | Bluesky | [email protected]

This episode, we discuss some of the challenges in using large language models (LLMs) for the task of summarising inpatient encounters. 

More details in the show notes on our website.

Episodes | Bluesky | [email protected]

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

00:30 - Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study

06:05 - Automation Bias in Large Language Model Assisted Diagnostic Reasoning Among AI-Trained Physicians

10:15 - Emerging algorithmic bias: fairness drift as the next dimension of model maintenance and sustainability 

15:20 - Evaluating Large Language Model Diagnostic Performance on JAMA Clinical Challenges via a Multi-Agent Conversational Framework

More details in the show notes on our website.

Episodes | Bluesky | [email protected]

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]

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]

In this episode, we’re lucky to be joined by Alexandre Sallinen and Tony O’Halloran from the Laboratory for Intelligent Global Health & Humanitarian Response Technologies to discuss how large language models are assessed, including their Massive Open Online Validation & Evaluation (MOOVE) initiative.

0:25 - Technical wrap: what are agents?

13:20 - What are benchmarks?

18:20 - Automated evaluation

20:10 - Benchmarks

37:45 - Human feedback

44:50 - LLM as judge

Read more about the projects we discuss here:

Meditron

Listen to the LiGHTCAST including their recent excellent outline of the HealthBench paper

More details in the show notes on our website.

Episodes | Bluesky | [email protected]

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]