Operating room technology
Surgery conducted by a robot, with a medical worker looking on.
“We are not even vaguely there in replicating the way that the mind – the human mind – operates in terms of solutions, in terms of pattern recognition,” Rodriguez y Baena stresses. “It’s the difference between a mouse and a human being – in fact, no, not a mouse, even smaller – I’m talking multicellular organisms.” What does AI in the operating room look like today? Broadly speaking, advanced technologies support one of two things when it comes to surgery: manual dexterity and decision making. As Rodriguez y Baena explains, huge strides have been made over the last two decades in the use of dexterous robots in the operating room. “20 years ago, just getting a robot into the operating theatre was a great achievement – now, there are plenty of examples of commercial systems that are making it through to the main league.” Cutting-edge robots with compliant, soft, delicate structures are beginning to perform surgery not only on bone but on soft tissues, while minimally invasive operations are steadily becoming easier thanks to robotic scaling. At the other end of the surgical spectrum, deep learning algorithms are becoming vital tools in clinical diagnostics. Take imaging technologies (just one among a number of AI diagnostic tools), which, as Rodriguez y Baena explains, could help to drastically reduce the time it currently takes to make diagnoses using classical methods of biopsy. “To this day, for the greatest majority of pathological diagnosis, you go in with a little guillotine and you take a piece of tissue. You give this to the runner, the runner takes it to the lab, the lab does their histology in, best-case scenario, 15–30 minutes – in the longest case you have to wait three days – and then you make your diagnosis. Now, if you could make that a one-step process, so that you go in, you diagnose and you execute your resection at the same time, that would be amazing. And there are a whole range of technologies that are more or less
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maturing at this stage: Raman [spectroscopy], OCT, fluorescence imaging.”
Intuition and iteration
These machines are known as “iterative solvers”, which means that they take a starting value and generate sequences of approximate but systematically improving solutions for problem sets. But, as Rodriguez y Baena points out, they are just that: solvers. “You can have models that basically build some relations between input and output on physical assumptions, and then there are black boxes like machine-learning algorithms,” he explains. “But they don’t really solve anything about the underlying physics of a problem. They just basically look, they mine data – input, output; input, output – and try to figure out the relationship between them.”
“AI is not all things to all men. As long as we treat it for what it is, then I think it’s a very capable tool, but... you may risk losing, in the next generation, all those subtle skills that make a clinician.”
Using these solvers effectively is a question of understanding their limits, rather than overstating their capabilities. For all his confidence in the power of the human mind, Rodriguez y Baena does have some concerns when it comes to how we use these tools. “If I had one opportunity [to offer] caution,” he admits, “it would be this: AI is not all things to all men. As long as we treat it for what it is, then I think it’s a very capable tool, but when you start drifting into, ‘We’re going to use it to take away human error,’ and when you start to feed it all sorts of information without really taking a step back, then you may risk
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