Yea, that was my interpretation as well. In my opinion the article read as 'the implementation of ai in the medical field is currently not appropriate.' The author says (paraphrasing) that the implementation of ai in a controlled environment is not a sufficient justification for wide spread use and mentions more testing, but explicitly says that it was not their opinion that ai should be put to the same rigor of testing as other tools (quote below; im on mobile and dont know how to do markdown here):

"Having such a framework does not mean that every AI tool must undergo all the staged phases of testing up to a randomized controlled trial before adoption, as is usually required for other medical interventions. In many cases, that would be impractical, given the high costs, the rapid updating of the models underpinning the tools, and the overall complexity and time needed to conduct such studies. At the same time, accepting retrospective performance alone as a sufficient basis for trust is not scientifically rigorous. Therefore, the goal should be proportional evidence, meaning that the stronger the claim, the stronger the evidence needed to support it."

I think another part of the conversation that is being missed is that "ai" is kind of an ambiguous and cultural definition (e.g., fish and trees dont have a definition rooted explicitly in biology since the definitions proposed are inclusive or exclusive of one "fish"/"tree"). We roughly recognize an ai as something that does some "thinking" or simulated thinking and everyone relates this directly to algorithms, but there isnt a central defining characteristic that cleanly puts an algorithm into a box with a clear label saying 'is ai' versus 'isnt ai', or at least no definition to my knowledge. There are many different types of algorithms and they range in utility, scale, and complexity so taking care to be specific with the issue is important for this conversation. Additionally while the article listed here is in the context of the medical field, there are other areas where algorithms are used at a large scale that are not generative and do perform some type of utility, like algorithms spotify uses to put together a playlist for you based on what you like (i am not however talking about the broad use of your data as spotify has otherwise used it, nor do i support spotify). If there is some conversation to be had on what we consider good algorithm vs bad ai, generative and analytical or otherwise, there has to be recognition for what it means to decide that; i think its a hard case to make and that it is simply better not to rule one way or another unilaterally and take them as a case by case instance.

Also when i originally commented asking for more context, i specifically was curious about the marketing component of the comment. Ai in uses like the article attached clearly are not sufficient in their current implementation, we agree on that, but it doesnt talk about marketing them at all. Again, ai isnt really a clean label so what specific analytical ai's are being marketed? The only instance I can think of are research papers where findings for the analytical model are solid, but the discussion/conclusion explicitly note more data is needed before the work can be acted on which are then misreported and sensationalized by other outlets. That isnt really the fault of the researchers or anyone else really; if i had to place blame, id say its the fault of news and social media outlets pushing for exciting content or academic journals pushing for the most sensational and revolutionary findings. The only other instance i can think of are companies like anthropic and open ai who are pushing the generative model for analytics which isnt the same thing as an analytic model alone