7 ms·
Some initial thoughts as a practicing radiologist: - This looks really cool and I hope they keep innovating on this. I love seeing new modalities develop and d
by jmhmd 4mo ago
Some initial thoughts as a practicing radiologist:
- This looks really cool and I hope they keep innovating on this. I love seeing new modalities develop and despite my (many) reservations and criticisms, if even one good use case comes out of it that truly helps people, it's tech money well spent imo.
- They show the reconstructed images as though they are a low resolution CT, and promise that quality will improve as they iterate. This is cool, but ultrasound is not CT. Ultrasound cannot image the lungs, as they are filled with air. You cannot find bone lesions, as the sound waves do not penetrate the cortex. You cannot image many structures in the abdomen if they are surrounded by gas-filled bowel. The brain is encased in bone, so you might get some penetration but it will be very limited. Even with theoretically perfect AI reconstruction, these scans will not be true "full body" in that there will be structures that are not reliably imaged. Imagine paying for weekly full body scans for years, everything looks fine, then its the lung cancer surrounded by air and invisible to ultrasound that kills you (that's why we use CT for lung screening!)
- The images they show are very cool, and do appear to show the correct structures. I realize this is early, but fuzzy shapes of organs is very, very far from medically useful. The whole point of screening is to identify problems early, often by definition, small. This technology looks like it will be best for seeing large, superficial (close to the skin) structures, whereas for effective screening, you want the opposite - small, deep structures.
- "Incidentalomas" or unexpected, probably benign, findings are annoying to physicians, but I in general have no problem with people collecting data on themselves where they can. To me it's similar to heart rate monitors or home blood pressure cuffs. The main issue here is education, so that patients know what the data is and is not telling them. The more complex the data, the more difficult that is.
- Many people mistakenly believe that early diagnosis is the final boss in medicine, that if only we could find every cancer early we could prevent all those deaths. There are, in fact, many, many other hurdles and bottlenecks. Many chronic, expensive diseases do not have clear imaging manifestations. The claim that "it's completely possible that with enough early imaging in the future, the world could avoid 30% of all deaths and 50% of all healthcare costs", I think, to any practicing physician, would sound completely divorced from reality.
- nixie 4mo agoCouldn't have said it better.
- conroydave 4mo agothis is why i always come to hacker news for the expert opinions. thank you for being critical yet optimistic.
- b40d-48b2-979e 4mo agoYou don't know if the opinion is expert. You don't know who that person is or what credentials they even have. Blindly trusting something that sounds right is a terrible way to inform yourself.
- arcticfox 4mo agoBayes Theorem...the chances this rather milquetoast and balanced analysis was written by someone with no knowledge is vanishingly low IMO.
- b40d-48b2-979e 4mo agoWith LLM tools being widely available, it's extremely high, imo.
- pbhjpbhj 4mo agoIdea for a website/documentary -- have experts respond to a piece of news, or provide commentary. Put a few expert pieces alongside a few LLM outputs, have people guess/work out which is which. Have the same people tell you why. If on a website, rank the results; present the 'how I worked it out' info for the best spotters (and you could interview them). Keep the answers secret for a few weeks, then reveal them in a way that the game is still playable. It's repeatable, every few months you could interview new experts (or the old ones again), get new models. Kinda like the critical thinking version of images of a pelican on a bike.