Comment by sebmellen

15 days ago

It’s wild how good it is at explaining nuanced and complex health topics.

As an example, I recently had an OCT scan of my retina because I'm a “glaucoma suspect.” I'm a glaucoma suspect because I have an unusually high cup-to-disc ratio, which is a pattern typically seen in glaucomatous patients.

As part of this OCT scan, you get a report back on your RNFL, which is basically a report on the health and thickness of your optic nerve(s) and retina(s). (Dramatically oversimplifying, of course.)

My ophthalmologist struggled to explain my OCT scan results to me and instead sort of shrugged and said, "It's probably because you're a high myope, but we'll keep you coming in once a year.”

But my scans look horrifically bright red — meaning I'm in less than the first percentile of retinal thickness for my age. Obviously concerning enough to do my own research…

ChatGPT 6 Pro and Opus 5.5 have struggled to make any sense of the clinical implications of these scans. In contrast, Argon was the first model to:

1. explain how the OCT machine works

2. detail the physiological mechanism that causes the cupping and thinner RNFL layers in my scenario

3. sum it up into a clinical recommendation (similar to what my ophthalmologist recommended, with the addition of basic supplementation and lifestyle modification changes)

4. provide an interactive visualization of the topography of my eye vs a glaucomatic eye: https://optic-nerve-3d.netlify.app/

I'm blown away.

How do you know whether the explanation is correct or just sounds correct?

  • Many times you lack the context or words, but after it gave them to you, you can look up papers and figure out if it matches. Same as you do with human doctors to decide if you need a second opinion.

    • Yes. I guess most of the HN crowd can do this reliably, but most people cannot, and that's why doctors hate when patients google around and then lecture them based on a 5-minute WebMD skim.

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  • If you have a solid self assessment of what you know, you can consider when information is supported by what you know. For the parts that aren't, you consider how critical they are, and identify the area that needs a deeper dive, where you either link it back to other areas you have confidence in your own knowledge or work to expand your own knowledge (using other resources). Then cross check knowledge. The cheap way you can do easily is to cross check between AIs/harnesses, because hallucinations happen randomly, so two different models in two different harnesses having the same hallucination is extremely rare. The more expensive option, which should be used when the information really matters, is building your own knowledge. Have the AI help guide you to resources, but not be the resource. While using non-AI resources, check you knowledge against them and the AI. Test yourself with classical resources where it really matters.

    Part of it is using good judgment. Is the AI's claim reasonable if it came from another person? If some random person gave you a 2 page paper proving P=NP, would you even spend time to look at it, or would you simply toss it without caring to understand what error they made. Much like how scientists (and even the patent office) simply refuse to entertain any perpetual motion machines. Maybe if an expert in the field proposed it, and even that would depend upon how ("Hey, I found a source of free energy!" vs "Hey, I'm working through these equations and the energy term isn't balancing, can you help me find what energy source I'm missing?").

    This does pose a problem. Those who are the best at understanding the limits of their knowledge and at teaching themselves need the AI's help the least but are in the position to be able to use AI with the best chance of not being misled. Those who lack these skills and would benefit the most from a personal educator, thus receving the biggest benefit in theory, are the ones most likely to fall victim to the errors made by the AI.

  • I mean, in this case it linked me to many sources, including the manual for the OCT machine used. One thing that even my ophthalmologist might not know, buried in the PDF manual, is that the sample size only includes 5 people under the age of 35 (skewing my "1st percentile" results more aggressively).

    But you're right that you can't be 100% certain. Isn't that true for human-to-human explanations too?

  • How do you know that for a web site? You have to research and cross check, and talk to a real doctor.

  • Ask a doctor?

    • +1. I've had successes from LLM-as-physio, but continuing to see the physio to make sure I'm not going mad.

      "Just see a physio" misses that:

      * I want the physio's advice during workouts

      * I want the physio's advice during moments of pain

      * I want the physio to explain things in more detail, but my 30-60 minute appt is not enough, and it's difficult/expensive to book them for longer.

Yeah my personal experience has been that Gemini is much more useful for trying to understand complex health issues than OpenAI or Anthropic models. The standard of proof just seems to be much better calibrated, in terms of understanding that a potential treatment that is potentially dangerous probably needs more evidence backing it than another potential treatment without much downside risk.

  • Absolutely. As one of my physician friends told me: “half the time, the best plan is, ‘We'll have to wait and see.’”

    But, as a patient, it's hard to find comfort in that. I think that might be what drives the craziness in “non-traditional” approaches to treating disease. Could it be that people using fruititarian diets and essential oils for cancer are doing so because they don’t understand enough to bear a “wait and see” situation?

    Argon explained the problem to me in such depth that I could be content with my basic treatment plan. ChatGPT went way off the deep end, suggesting things like high-dose niacinamide treatment (which is admittedly being evaluated in clinical trials, but comes with a host of other side effects).

    • To be fair, try getting cancer and then consider how you feel about “wait and see” even when you understand enough to know it’s the advice with the best evidence.

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This is a great example of something that two years ago was unthinkable. I'm very impressed.

Thanks for sharing and I hope your eyes remain fine.

I wonder how much that would cost to make in 2020 developer dollars

  • I have no idea how I’d have even started on a project like this in 2020… certainly not by writing raw WebGL — ha!

    • It's genuinely unthinkable... this sort of technology opens up a universe of bespoke micro-explainers and apps that meet you exactly where you are, for your exact, specific needs. I don't think we've ever seen a quantum leap in technological advancement that results in such a multiple orders of magnitude reduction in input effort related to volume and quality and type of output... ever. I mean, innovations like electric light, radio, television, the internet, and smart phones are huge transformative tools that similarly changed the fabric of what's possible. But this technology compresses work that would have taken pyramid-building effort and cost and condenses it down to... minutes of matrix multiplication for pennies worth of tokens. The mind boggles.

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Guessing they've been going all in on the research angle for health stuff and might have an actual product for medical professionals soon