Reviews are a mess

The medical industry is going to massively shift with AI. It already is in some practices. I've already personally used it to help my wife analyze her bloodwork, and set up a plan to address the findings. Perplexity has a smartphone app, so I can just take pictures of the bloodwork readout, and put it right into a Perplexity Deep Research thread. I give it extra context around the sex and age of the person, give it an overview of the issues they are having, then tell it to analyze the bloodwork using that context. Then it gives a really detailed, but still succinct, overview of what the results show. Then you can do followup questions about supplement regimens to correct it. It's still not at the level of a human for synthesizing whole plans that are as good as an experienced human would. However, it's great at analyzing the plan I come up with using my knowledge, then honing that in.

It's the same for new product development. We are using AI every day in the R&D and lab side of things. Normally what would happen when I got some analytical results that didn't make sense would be I would post the chromatograms or HPTLC plate results in our lab group chat. Then I would ask my team what they thought, and we would have a back and forth trying to figure it out. That works okay, but it always took a long time to solve issues, because it was a manual process of my team and I trying to analyze some result. Now I just head right to Perplexity and feed it the data. Then I have a tentative answer in minutes. Then I can take that result and put it in the chat, and my team and I can discuss it. This allows us to solve complex chemistry issues much much quicker than before. I can literally feed it chromatograms, give it the methods we used and data we know, and ask it to analyze what unknown peaks are. I can feed it the HPTLC plate of a botanical, then feed it the chromatograms from the assay, and have it analyze the two together. Then once I have a better idea of what I am looking for, I can do follow up queries on larger contexts surrounding why that result might have happened. Let me give you a recent example with Primavie.

Here is a snippet of a thread where I was analyzing the current batch of Primavie to an old one.

I had already fed it HPTLC data, and followed up with FTIR data. This one was after I fed it new UPLC-MS data.

Here is where I ask it what is missing.

So it helps try to analyze which compounds are different between the batches.

Here is where I ask it what specific peaks might be.

You can see I treat it like a conversation I would have with one of my PhD scientists. However, just like anything else, it's not perfect. You have to use it as a tool, and make sure you understand what you are discussing, or it can miss some things. It was at this point where I said the data wasn't making sense with its explanations. I thought it had mixed the lines up, and was giving me explanations in reverse, so I just asked it.

Here is where I ask it to explain why the larger lines don't mean the new batch has more of those compounds.

So you can see it corrects itself. Even humans can mix things up sometimes, and AI is no different. This is what most people fundamentally miss. AI is a tool, but like any tool, you have to know how to use it. You don't just pick up a drill and have a fully built dresser automatically. You have to use the drill properly. The same goes here. You have to watch what the AI is putting out, and try to spot instances where it might have missed something, mixed something up, or synthesized something out of thin air. That's still a thing with the current models. Sometimes they will make things up out of thin air, and you have to push back on them to figure that out. If you just turn AI loose and leave it to its own devices, you are not going to get accurate info. You need to really keep a handle on what it is doing, and use your own brain in the process. Only then will you get real use out of it. Now that is likely to change as the models improve, and that might not be too far off. However, the general concept is always going to be there. Just like monitoring your own employees to ensure they are on task and doing things right, you are going to have to monitor AI in a similar way.