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

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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