5 ms·

Right, "horses not zebras" means "if it has four hooves and a mane check if it's a horse first", not "your patient definitely doesn't have a rare condition". I

Right, "horses not zebras" means "if it has four hooves and a mane check if it's a horse first", not "your patient definitely doesn't have a rare condition". I know so many with chronic conditions that were brushed off by doctors or misdiagnosed that only got the right diagnosis later when their conditions worsened.

londons_explore 4y ago
This process of most doctors is: 'gather up available data/symptoms/test results from this patient, then compare against medical research to see which diagnoses fit and which is most likely'. Since the process is so mechanical and so stats-driven, I think statistical computer models or even AI would do a much better job of it than your average doctor. But due to the medical field being slow to utilize new methods, we don't see any of that yet, and the end result is far more people get false diagnoses than would otherwise. We can accept a human making a poor judgement sometimes, but we do not accept a machine doing the same, even if it's right more of the time.
- Retric 4y ago
  People have been trying to make medical AI for years and have just failed. More funding could help, but it’s just a hard problem.
  - cjbgkagh 4y ago
    A lot of those efforts were based around expert systems and failed for many of the same reasons many other expert systems suffered from. I would suggest that a lot of medical questions are now answered by Dr. Google which implicitly does a lot of behavioral modeling / statistics in their machine learning and is already very successful.
  - opo 4y ago
    Unfortunately, in medicine, there is evidence that just because a computer program is better then a Dr at diagnosing a patient is no guarantee it will be used. The classic example here was the MYCIN expert system developed in the 1970s. MYCIN was shown to outperform infectious disease experts by 1979 in a blind testing: >... Eight independent evaluators with special expertise in the management of meningitis compared MYCIN's choice of antimicrobials with the choices of nine human prescribers for ten test cases of meningitis. MYCIN received an acceptability rating of 65% by the evaluators; the corresponding ratings for acceptability of the regimen prescribed by the five faculty specialists ranged from 42.5% to 62.5%. The system never failed to cover a treatable pathogen while demonstrating efficiency in minimizing the number of antimicrobials prescribed. https://jamanetwork.com/journals/jama/article-abstract/366606 https://jamanetwork.com/journals/jama/article-abstract/36660... https://en.wikipedia.org/wiki/Mycin https://en.wikipedia.org/wiki/Mycin
    - cfu28 4y ago
      There are a lot of examples of these "expert systems" in medicine that seem to do well in research, but fail in other ways. We're seeing some of these failures again these days with ML models in medicine, especially bridging the gap between the proof-of-concept and production systems. https://www.jstor.org/stable/3666074 https://www.jstor.org/stable/3666074 https://aimi.stanford.edu/events/bridging-ais-proof-concept-production-gap-andrew-ng-phd https://aimi.stanford.edu/events/bridging-ais-proof-concept-...

londons_explore 4y ago

This process of most doctors is: 'gather up available data/symptoms/test results from this patient, then compare against medical research to see which diagnoses fit and which is most likely'. Since the process is so mechanical and so stats-driven, I think statistical computer models or even AI would do a much better job of it than your average doctor. But due to the medical field being slow to utilize new methods, we don't see any of that yet, and the end result is far more people get false diagnoses than would otherwise. We can accept a human making a poor judgement sometimes, but we do not accept a machine doing the same, even if it's right more of the time.

Retric 4y ago
People have been trying to make medical AI for years and have just failed. More funding could help, but it’s just a hard problem.
- cjbgkagh 4y ago
  A lot of those efforts were based around expert systems and failed for many of the same reasons many other expert systems suffered from. I would suggest that a lot of medical questions are now answered by Dr. Google which implicitly does a lot of behavioral modeling / statistics in their machine learning and is already very successful.
- opo 4y ago
  Unfortunately, in medicine, there is evidence that just because a computer program is better then a Dr at diagnosing a patient is no guarantee it will be used. The classic example here was the MYCIN expert system developed in the 1970s. MYCIN was shown to outperform infectious disease experts by 1979 in a blind testing: >... Eight independent evaluators with special expertise in the management of meningitis compared MYCIN's choice of antimicrobials with the choices of nine human prescribers for ten test cases of meningitis. MYCIN received an acceptability rating of 65% by the evaluators; the corresponding ratings for acceptability of the regimen prescribed by the five faculty specialists ranged from 42.5% to 62.5%. The system never failed to cover a treatable pathogen while demonstrating efficiency in minimizing the number of antimicrobials prescribed. https://jamanetwork.com/journals/jama/article-abstract/366606 https://jamanetwork.com/journals/jama/article-abstract/36660... https://en.wikipedia.org/wiki/Mycin https://en.wikipedia.org/wiki/Mycin
  - cfu28 4y ago
    There are a lot of examples of these "expert systems" in medicine that seem to do well in research, but fail in other ways. We're seeing some of these failures again these days with ML models in medicine, especially bridging the gap between the proof-of-concept and production systems. https://www.jstor.org/stable/3666074 https://www.jstor.org/stable/3666074 https://aimi.stanford.edu/events/bridging-ais-proof-concept-production-gap-andrew-ng-phd https://aimi.stanford.edu/events/bridging-ais-proof-concept-...