medicine

The article speculates on how future advancements in math, physics, chemistry, and biology could make current medical practices appear barbaric by 2040.

HealMed is an expert-reviewed benchmark for evaluating large language models in medicine across nine languages, developed by medical experts to assess multilingual performance in clinical tasks.

The paper develops a generative analogy between clinical translation in medicine and building reliable machine learning systems, proposing a new form of ML reliabilism based on reliabilist epistemic warrants.

A case report details how a toddler's rare Balamuthia mandrillaris brain infection was misdiagnosed as Takayasu arteritis, delaying treatment until fatal brain damage occurred. The report aims to help doctors identify future cases earlier.

A discussion on whether AI can accelerate medical science, potentially treating or curing chronic conditions in the coming decades, and whether a golden age of medicine is realistic.

The author argues that AI intelligence is not the main bottleneck for real-world progress, especially in medicine, where regulation and clinical trials remain the limiting factors despite AI hype.

Doctors report a rare case of Milwaukee Shoulder Syndrome in a construction worker, whose shoulder joint was completely destroyed and the humeral head absent on X-ray.

An interview with pediatric allergist Dr. Zachary Rubin, who uses social media to counter medical misinformation with evidence-based science communication.

An in-depth article from Works in Progress exploring how microbubbles, when burst with ultrasound, can overcome biological barriers like the blood-brain barrier to improve drug delivery, with potential applications in treating brain diseases and other conditions.

Derek Thompson highlights recent medical breakthroughs including an effective GLP-1 drug from Eli Lilly and a promising new pancreatic cancer drug from Revolution Medicines.

A tweet discussing a conversation with Stanford Medicine's Dean Lloyd Minor about the potential of AI in healthcare to capture continuous patient lived experience, rather than relying on fragmented clinic recall.

The article argues that AI in medicine may fail due to poor calibration and inability to express uncertainty, rather than lack of eloquence, and calls for features that build trust.