Medical Attention

Here’s a quick wrap of the three papers we found interesting over the last few weeks with some take home points. 01:00 - Technical wrap - ChatGPT and Claude for healthcare, AI scribes 07:40 - What is decision theory? 33:20 - Evaluation of performance measures in predictive artificial intelligence models to support medical decisions Join the conversation on our new LinkedIn page: https://www.linkedin.com/company/medicalattention/ Episodes | Bluesky | info@medicalattention.ai

Here’s a quick wrap of the three papers we found interesting over the last few weeks with some take home points. 00:30 - LLMs Can Do Medical Harm: Stress-Testing Clinical Decisions Under Social Pressure 09:30 - Measuring provider-level differences in perioperative workflow using computer vision-based artificial intelligence 12:55 - Heterogenous effect of automated alerts on mortality Join the conversation on our new LinkedIn page: https://www.linkedin.com/company/medicalattention/ Episodes | Bluesky | info@medicalattention.ai

Here’s a quick wrap of the three papers we found interesting over the last few weeks with some take home points. 00:30 - Ambient AI RCTs A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being Ambient AI Scribes in Clinical Practice: A Randomized Trial AI Scribes Are Not Productivity Tools (Yet) 09:55 - Language models cannot reliably distinguish belief from knowledge and fact 17:20 - Extracting social determinants of health from electronic health records: development and comparison of rule-based and large language models-based methods More details in the show notes on our website. Episodes | Bluesky | info@medicalattention.ai

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 | info@medicalattention.ai

This episode, we discuss some of the challenges in using large language models (LLMs) for the task of summarising inpatient encounters.  00:30 - Technical wrap - new models, MiT’s State of AI in Business 2025 12:40 - Medical summarisation 19:30 - Context Rot: How Increasing Input Tokens Impacts LLM Performance 30:50 - Verifiable Summarization of Electronic Health Records Using Large Language Models to Support Chart Review 40:00 - Evaluating large language models for drafting emergency department encounter summaries 42:25 - Evaluating Hospital Course Summarization by an Electronic Health Record-Based Large Language Model More details in the show notes on our website. Episodes | Bluesky | info@medicalattention.ai

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 | info@medicalattention.ai

Here’s a quick wrap of the three papers we found interesting over the last few weeks with some take home points. 00:30 - An Electrocardiogram Foundation Model Built on over 10 Million Recordings 07:10 - Zero-shot Large Language Models for Long Clinical Text Summarization with Temporal Reasoning 14:15 - Diagnostic Codes in AI prediction models and Label Leakage of Same-admission Clinical Outcomes 16:50 - Evaluating reasoning LLMs’ potential to perpetuate racial and gender disease stereotypes in healthcare More details in the show notes on our website. Episodes | Bluesky | info@medicalattention.ai

Here’s a quick wrap of the three papers we found interesting over the last few weeks with some take home points. 1:00 - Clinical knowledge in LLMs does not translate to human interactions 06:45 - From Tool to Teammate: A Randomized Controlled Trial of Clinician-AI Collaborative Workflows for Diagnosis 11:55 - Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study More details in the show notes on our website. Episodes | Bluesky | info@medicalattention.ai

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 Learn about the MOOVE or contact our team if you'd like to be involved Listen to the LiGHTCAST including their recent excellent outline of the HealthBench paper More details in the show notes on our website. Episodes | Bluesky | info@medicalattention.ai

Here’s a quick wrap of the three papers we found interesting over the last few weeks with some take home points. 0:35 - Superhuman performance of a large language model on the reasoning tasks of a physician 06:20 - MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks 11:45 - Identifying and mitigating algorithmic bias in the safety net More details in the show notes on our website. Episodes | Bluesky | info@medicalattention.ai

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. 01:30 - Patient Reactions to Artificial Intelligence-Clinician Discrepancies 06:50 - AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction  11:10 - Large-scale Local Deployment of DeepSeek-R1 in Pilot Hospitals in China More details in the show notes on our website. Episodes | Bluesky | info@medicalattention.ai

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 | info@medicalattention.ai

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 | info@medicalattention.ai

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) 10:25 AMIA Year in Review Episodes | Bluesky | info@medicalattention.ai

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 | info@medicalattention.ai  

In this episode, we discuss more of the technical aspects of LLM implementation in healthcare, including the following topics: Embeddings Retrieval augmented generation (RAG) Fine tuning Low-Rank Adaptation (LoRA) Small models Quantisation Encoder and decoder models Multimodal transformers Oh my!   Episodes | Twitter | info@medicalattention.ai  

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  Difficulties of model evaluation Concerns regarding bias and equity Governance and monitoring of LLMs in a healthcare settings   Episodes | Twitter | info@medicalattention.ai  

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 Discuss a brief development of LLMs to this point How LLMs are trained Some limitations - privacy, accuracy, provenance   Episodes | Twitter | info@medicalattention.ai  

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 Overview of the development of neural networks and use in healthcare How neural networks are currently used in medicine Limitations and considerations for their use   Episodes | Twitter | info@medicalattention.ai  

Welcome to Medical Attention. This episode is an introduction to artificial intelligence and machine learning for people in healthcare with no technical background.   Episodes | Twitter | info@medicalattention.ai