Baseline Observations

How Google Deepmind is researching the frontiers of medical A.I.

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 heights healthcare may reach when A.I. is leveraged intelligently and thoughtfully. Our conversation spans from the process of building smart models to the insights David's research has found about how to best implement them. We hope you enjoy it! If you want to read more about David's work, here's some of their recent preprints: https://arxiv.org/abs/2507.15743 [https://arxiv.org/abs/2507.15743] https://arxiv.org/abs/2505.04653 [https://arxiv.org/abs/2505.04653] If you have any feedback or ideas for future guests, please send us a message at baselineobservations@gmail.com.

What companies and influencers don't tell you about their products

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 idea to try. Separating out the useful from the useless is crucial - in this episode, Arham and I talk about some of the things we’re thinking about when we try to do that. If you’re enjoying the podcast, please give us a rating! It goes a long way in helping us grow larger. We always welcome any and all feedback - leave a comment or send us an email at baselineobservations@gmail.com.

Why making medical decisions based on the best evidence is both an art and a science, featuring Dr. Anil Makam

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 context overrides published literature, and how new technologies and data sources can provide novel value to the medical evidence base. You can find Dr. Makam at @AnilMakam [https://x.com/anilmakam] on X. If you have any feedback for the show, please leave a comment or reach out at rjhawar227@gmail.com.

How standardized should medicine actually be?

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 that guidelines are important and that individual context should remain supreme, but how do we draw the line on what's apples and what's oranges here? Beneath this all - is competition in healthcare actually good? This conversation touches on all of the above, and truly was one of the episodes where both Arham and I decisively changed our minds. If you have any feedback, please leave a comment or reach out at rjhawar227@gmail.com.

Should pre-medical training just be a statistics degree?

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 time has come for hard sciences to be kicked out of the pre-med equation and more rigorous statistical training to be added in. While discussing this, we confront the larger question of whether understanding first principles will even be relevant as the world continues to progress. All feedback is always appreciated! Please leave a comment here, or reach out at rjhawar227@gmail.com.