Baseline Observations

After a long hiatus, we return with one of our most interesting episodes yet! Arham and I were delighted to chat with David Stutz, Ph. D., whose work at Google Deepmind has been absolutely trailblazing in the field of medical A.I. Unlike most A.I. conversations where I walk away with existential dread about going to medical school in the age of LLMs, I left incredibly inspired about the new height...

After enough "DOCTOR'S DON'T WANT YOU TO KNOW THIS" headlines, it's about time we flip the script. Let's start here: red light therapy does not do much to fix hairlines. Trust me because...well, it's obvious. It is impossible to practice (let alone pay for) everything you see online that’s claimed to be good for you, and we’re not even sure that it would be a good ide...

Applying medical research at the bedside can be difficult. After all, you don't inspire the most confidence in patients when you tell them their treatments are backed by p values of 0.049. Dr. Anil Makam schools me here on how medical evidence should actually be applied in practice. We cover how doctors can make sure that their everyday decisions are based on the best evidence, when clinical c...

As a general rule, lowering your standards is almost never a good idea. But when you're giving relationship advice to a humongous national corporation that dictates nearly everything in American healthcare, maybe the general rules don't apply... We tackle an increasingly pressing issue here - how much of medicine should be standardized and centrally regulated? It's easy to say both tha...

All premeds take o-chem, some premeds take p-chem, but should any take...no-chem??? As medicine has become more evidence-based and biologically sophisticated, the job description of a doctor has changed dramatically over the past half-century. Good clinical practice involves a lot more of inspecting p-values and effect sizes than it ever did before, and there's an argument to be made that the...

Physics haters be warned… Dr. Travis Zack is a practicing oncologist and assistant professor at UCSF, where he heads a research lab working on implementing A.I. tools in the electronic health record. He is also head of A.I. at UCSF's Comprehensive Cancer Center, and a clinical consultant at OpenEvidence, one of the world's fastest growing and most innovative A.I. powered clinical decision support...

There may be only one thing that lasts longer than ketchup: clinical trials from the 1990's. But at a certain point, even these must expire. How do we know when it's time to put old knowledge to new tests, and is there a way to do this ethically and efficiently? Arham and I tackle this as we grapple with an hilariously unsettling truth: All roads may lead to Rome, but all healthcare startu...

As AI/ML methods progress at an incredible pace, what advancements in medical therapies can we look forward to? To explore this, Arham and I discuss some of the fundamentals behind how new drugs are discovered and some of the seminal advancements that machine learning has brought to this field. We then predict how this will impact both clinical medicine and the economics of the pharmaceutical indu...

Many widespread practices in medicine have never been directly shown to make people live longer, happier, or healthier. Arham and I discuss why this is the case. Is this a fair standard, or is it impossibly difficult to reach? I present the stances held by a few different camps in medicine, and Arham responds with a statistician's perspective on them.

With all the media recently about United Healthcare and its widespread claim denials, we discussed why health insurers deny care (HINT: it's not because it saves them money). We also discussed why current regulations in the health insurance market actually incentivize health insurers to increase healthcare spending, not decrease it. We would love to hear any and all feedback in the comments.

We discuss whether medical training should have a more dedicated emphasis on teaching statistics, and broadly touch on the implications of statistics in medical decision making.