[ps2fats]

May I suggest, in response to your sentiment that applications of AI to medicine are lacking, is that you are seeing applications replace current medical practices. An AI diagnosis of a medical image seems redundant indeed, however in this situation a patient has seen a doctor out of complaints and has been sent to the radiologist for further investigation. This medical practice is reactionary, and suspicions are already present, so of course the AI isn't doing much useful here.

Alternatively, imagine a proactive medical world, in which preventative screenings are commonplace. Currently, the implementation of routine screenings without any complaints is prohibitively expensive on a large scale. This is because it requires manpower, and manpower is prohibitively expensive and the expense of manhours needs to be justified by a medical practitioner. However, AI can help in this proactive medical world by reducing the number of hours real people are looking through data to detect problems of patients, reducing the cost of routine screenings at large. Again, this wouldn't replace doctors, as you'd still need a specialist to analyze any positive hits, but it differs from your scenario in which the AI diagnosis seems redundant.

So, when preventative medical practices are more prevalent, the mass routine screening procedures will need help from machines to keep it cost effective, and that I believe is where this technology will find its application.

[dnautics]

I disagree. You don't really want to routinize that level of medical surveillance, due to the classical Bayesian predictive power problem. When you come in with a complaint, it changes the prior and is additional evidence to revise the diagnosis on top of the screening information.

What you do want out of AI is to flag areas of interest in imaging for example and help identify when records are at risk of being incorrectly normalized. Ideally, even if the end effect is marginal (say bumping accuracy from 80% to 90%), if it enables a workflow that decreases the exhaustion and frustration of the doctor you will want that in place.

Of course it could just as well be used as an excuse by management to increase any given doctor's throughput, so it might not work as you would want.

[dontreact]

Screening has been shown to be effective for lung cancer. With enough data, we can improve the posterior enough for certain applications that we don’t need the stronger prior of complaints.

Over time as AI improves, more and more diagnoses can look like this.

[twic]

Sounds good, doesn't it? But you have to have a really, really low false positive rate for this to work out. This is already a problem with mammography:

https://www.cochrane.dk/news/new-study-finds-breast-cancer-s...

[rahulnair23]

Health policy is fraught with counter-intuitive phenomenon - and screening is one of them.

Seems like it should help, but in practice leads to over-diagnosis.

For example - Cancer rates jumped in Korea after screening with no impact on patient outcomes [1]. There are several others.

[1] Lee, J. H., & Shin, S. W. (2014). Overdiagnosis and screening for thyroid cancer in Korea. The Lancet, 384(9957), 1848.

[ps2fats]

You can hardly conclude that broadly screening populations are ineffective from this study. You have to consider, among other things, the treatments available for the given disease being screened and the cost of that screening program. If treatments for the disease already have a low success rate (what is low?), the timing of detection doesn't really help. Additionally, if the cost of the screening program is negligible (what is negligible?), then even successfully treating a few patients may be worth it.

[adolph]

The current consensus about over-diagnosis (as I understand it) is that when there is a significant false positive rate and the cost of proving the positive false is high (in money, time, effort, worry), the screening program is not helpful. Some go further to say that low cost screening drives some of the high cost to outcome ratio in the US. I'll try to find a cite in my textbooks if you are interested.

[srean]

I think the issues are deeper than that of false positives. Its possible that transient diseases get detected that would have fixed themselves without any treatment. Insead of a non-treatment one now has to deal with the side-effects of the interventions applied.

[gdebel]

This is exacerbated by the fact that if the AI told the doctor that there is a doubt, no doctor will take the risk of not doing a biopsy / scanner / MRI / surgery (depending on the case). Because, how would you defend yourself in front of the judge ? This is something we always have in mind.

This is how you end with false positives and over-diagnosis.

[dontreact]

This is a false blanket statement. Also one that could change as we start to see human+ai performance be better than just human performance.

For lung cancer screening, NLST showed a 20% reduction in mortality and now NELSON has shown even stronger results in Europe.

This “all screening is bad” is FUD in the medical field, frankly. Yes it has to be studied and implemented carefully, but to make blanket statements about screening as a whole is factually incorrect.

[rahulnair23]

I have not stated "all screening is bad".

Broad-based population screenings as the parent comment suggests, in my opinion, are.

I'm yet to see any clinically-valid distinguishing aspects that would suggest AI would add value to screening. Curious to hear evidence that drives your optimism of human+AI.

Just to state, the NELSON study [1] focuses on high-risk segments. Their paper also recommends a "personalized risk-based approach" to screening. This seems reasonable.

[1] https://www.nejm.org/doi/full/10.1056/nejmoa1911793

[dontreact]

The general thread here is about AI helping with a more proactive approach to medicine. Screening for high risk populations certainly falls under that.

You certainly said that screening leads to over diagnosis.

I think for screening, the best results are probably the upcoming prospective study from Kheiron.

https://www.kheironmed.com/news/press-release-new-results-sh...

[dontreact]

I suspect, btw, that the Google model in this paper https://www.nature.com/articles/s41586-019-1799-6

will show stronger performance. But Kheiron appears to be ahead as far as proving the value of the tool since they have actually validated prospectively.

[Gatsky]

I would differentiate prevention from screening. Screening is for early detection of a problem (for example, the Pap smear to detect pre-cancerous lesions on the cervix). Prevention prevents the problem in the first place (eg the vaccine against the human papilloma virus which causes cervical cancer).

Truly effective preventative measures are the apex achievement of medical science, and have simply deleted an unimaginable amount of human suffering from our modern lives. Vaccines and sanitation are the best examples. They are astoundingly cost effective measures, and are so good that in many ways they are unimprovable in any significant way. 2020 is yet another example of how important vaccine technology is to every single person on this planet.

Screening is nowhere near as beneficial as actual prevention. It is expensive, labour intensive, requires behavioural modification, definitely harms a significant proportion of people due to false positives, and in controlled trials only modestly improves hard clinical outcomes under the most charitable assumptions of compliance and follow up care.

You are proposing a model of 'high-touch' medicine, where people have a raft of continuously administered screening tests for a long list of conditions. This could only ever be applied to a small proportion of the world's population, and would require highly motivated and well educated patients. In my opinion, spending a day in a primary care medical clinic would disabuse you of the notion that this is a feasible or desirable outcome.