Oncology Consultations with Artificial Intelligence: Limits and Controversies
Oncology Consultations with Artificial Intelligence: Limits and Controversies
Can artificial intelligence be as good as a doctor’s judgment?
I’d be willing to bet that by now you’re already annoyed and thinking about how to respond in a way that aggressively counters me, yet doesn’t upset the moderators of this site—so you don’t risk getting banned—while still letting out some of the anger you’re feeling.
Let’s try, however, not to give in to our first impression and read on, because this text is based on personal experience and is not a summary of articles found online.
We’ll start with the general conclusion that artificial intelligence in medicine is the subject of many jokes and ironic reports questioning its usefulness, due to the wrong, dubious, or meaningless answers it has provided, which have sparked a great deal of controversy.
All of this information is entirely accurate and no one disputes it: artificial intelligence in medicine can produce major errors that may endanger patients’ lives and suggest entirely ill-advised decisions.
But is this the only truth?
How do you avoid bankruptcy if you run a private company based on artificial intelligence in medicine? Apparently, based on preliminary data, one might easily conclude that it is an unprofitable venture from the start, through which you can very easily lose credibility and a great deal of money.
And yet, that is not the case.
Why are the answers from AI models (regardless of the company that created them) so inaccurate when the questions come from the medical field?
The answer is usually hidden and relates to how the AI models were trained. No attempt has ever been made to create an AI model specialized in the medical field, so these models were “fed” information from various sources, without it always being organized logically. The large volume of data, often entered randomly, can generate these non-compliant responses that provoke either irony or anger.
The question is: if we change the way we “train” an artificial intelligence model, do we get different results? Although the answer may surprise you, it is yes.
How do we ensure that an artificial intelligence model’s reasoning resembles that of a doctor?
The answer is simple: the algorithm.
If you’re a doctor and a patient walks into your office describing some symptoms, there’s already a decision-making algorithm in your mind. Thus, once you have some information, you can fill in the bigger picture by asking additional questions about the presence of other symptoms or the characteristics of existing ones, which can point you toward a specific disease or a group of diseases. Once you’ve narrowed down the possibilities following your discussion with the patient, you begin to recommend certain tests that will either confirm or rule out your suspicions. Let’s not lose sight of the fact that, in the doctor’s mind, if-then-else algorithms are running at this moment: if the information aligns with their judgment, a specific option is chosen ; if not, they move on to the next step. An artificial intelligence model must operate on the same principle.
Now, let’s return to an everyday problem. You, too, use algorithms in everything you do, but they have likely become automatic and you no longer perceive them as such. Let’s take a simple example: you want to make yourself a cup of coffee. At this point, you go into the kitchen or the appropriate space and know that it’s on a certain shelf, in a certain spot. If it’s there, you take it and start putting it in a cup or the espresso machine. If you don’t find the coffee there, you assume it’s nearby and look for it. If you’re out of coffee, you think maybe you bought a new package, which is in a different spot. So far, we’ve had a series of logical decisions, made based on algorithms. Next, you need to add sugar (if you use it). You know where it is, check if you have enough, and if not, you have to look elsewhere. And here, too, there’s a whole series of if-then-else algorithms.
Now, if you wanted to teach an artificial intelligence model (basically, a robot) to make coffee, you have two options. First: take it into the kitchen when you want to make coffee, without telling it anything, just letting it watch your gestures. The results will be at best questionable at first; it will manage, at most, to imitate a few gestures. However, if you repeat this process several times, every morning, taking the robot with you, over time it will be able to make logical connections and draw certain conclusions.
The second option would be to take the robot into the kitchen from day one and tell it: “Look, now I’m going to this shelf, where I find the coffee, and from there I’ll take a few teaspoons. If I don’t find the coffee here, I’ll look for it somewhere else. If it’s not there either, I’ll check to see if I can find a new package in that area.” If you proceed according to the second option, you’ll be able to teach the robot much more easily, and it will be able to make coffee in a much shorter time.
The same reasoning applies to artificial intelligence models in oncology or in medicine in general. It’s a mistake to “feed” the model only with large databases; it needs to be fed with algorithms.
Where do we get these algorithms in medicine?
The answer is relatively simple, but difficult to implement: from medical textbooks, from information about studies, from specialized medical websites, etc.
Although at first glance a medical book is a text, it is not like a detective or romance novel. Beneath the surface of words and information written in paragraphs, there are algorithms that can be easily extracted by an artificial intelligence model. You can try a relatively simple experiment: take a page from a medical book, feed it to a more advanced AI model, and ask it to convert that page into an algorithm. You’ll see that, theoretically, it’s possible.
Now comes the big question: what do you do if you have a private company that uses AI in the medical field and is required to provide coherent answers? The answer, though not simple, is that you have to get to work. There’s no easy way to find this data from a single source; you have to create it yourself. Unfortunately, this involves a massive amount of work, similar to the work schedule Elon Musk would like to impose on his employees—namely, 9-9-6. (9:00 a.m.–9:00 p.m. / 6 days a week) Except, in this case, you’re motivated to do this work because it’s your own business.
After working for at least two years, you end up with a volume of data covering approximately 220,000 A4 pages filled with text. This text differs from that found in medical books, as it is condensed and presented in the form of algorithms. The volume is immense, equivalent to over 150–180 medical treatises, and if we were to make a comparison, the stack of A4-sized sheets of paper would be approximately 26 meters high (roughly the height of a 7-story building).
Do you think a human being, no matter how intelligent or how good a memory they have, could read and retain this amount of information?
DEFINITELY NOT.
WHAT DO WE HAVE RIGHT NOW?
An AI model that has the thinking algorithms of a doctor and can utilize a massive amount of information.
Did I forget anything?
YES, two things:
- One advantage is that the model no longer has hallucinations and can no longer give non-compliant answers, because it is “forced” to respond strictly from its own database.
- A disadvantage is that, as a doctor, you must verify the answer to authenticate it before giving it to the patient (current legislation requires this, and it is both correct and ethical).
Finally, I’d like to ask you to consider what will happen when quantum processors become commonplace ?