Machines, AI and the future of Medicine

Original article was published by Samuel A Donkor on Artificial Intelligence on Medium

Machines, AI and the future of Medicine

Since the term was coined in 1956, “Artificial Intelligence” has endured a lifetime of misunderstanding. When most people hear the words “artificial intelligence,” the first thing that comes to mind is this all-knowing, sentient machine that can outsmart and overpower any human being ; a scary machine that will take away all our jobs. Even if that ultimately ends up being true, I do not believe we are anywhere close to that scenario.

The root of the problem lies in the interpretation of the word “intelligence.” Artificial intelligence is mathematical computation, not human intelligence.Whether you are aware of it or not, algorithms(mathematical computations) are becoming a ubiquitous part of our lives and major industries as well. Although most sectors of the world are well into the Fourth Industrial Revolution, which is centered on the use of AI, medicine is still stuck in the early phase of the third, which saw the first widespread use of computers and electronics. This is as a result of dismissive attitudes common in medicine, making change in the field glacial.

— giving clinicians time back to work with their patients …

I am excited about the future, about how artificial intelligence will democratize healthcare and how artificial intelligence and deep learning will bring tremendous precision to diagnosis and prognostication. This isn’t to say they will replace humans. For the most part, in the medical profession, AI will be an accelerant and enabler, not a threat. It would be good business for AI companies as well to help, rather than attempt to replace medical professionals. What AI will provide is a recommendation, one that is perhaps more accurate than it has ever been. The graph below shows the progress in reducing the error rate over several years, with ImageNet wrapping up in 2017, with significantly better than human performance in image recognition. The error rate fell from 30 percent in 2010 to 4 percent in 2016

Error rates on the ImageNet Large-Scale Visual Recognition Challenge. Accuracy dramatically improved with the introduction of deep learning in 2012 and continued to improve thereafter. Humans perform with an error rate of approximately 5%. Adapted from Curtis P Langlotz et al, ‘‘A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop’’

Google has developed an artificial intelligence (AI) system that has shown, in some cases, it can detect breast cancer better than radiologists, according to a study published recently in Nature.

Understandably, this has raised questions about the future role of doctors and what unforeseen impact AI will have on the practice of medicine. But as I said early on, AI will not replace doctors at least not ‘’clinicians without patterns’’.

“physicians suffer from considerable stress caused by facets of their job that have little to do with actually providing personalized patient care.”

Unlike humans who get tired, have bad days, may get emotional, sleep deprived, or distracted, machines are steady, can work 24/7 without vacations, and don’t complain.The role of AI is to restore the original duty of the physician — caring for the patients. As Francis Peabody wrote in 1927, “The secret of the care of the patient is caring for the patient.” The glaring example is in the electronic healthcare record systems (EHRs) currently in use in most hospitals. These EHRs were designed for billing, not for ease of use by physicians and nurses. They have affected physician well-being and are responsible for burnout and attrition; moreover, they have forced an inattentiveness to the patient by virtue of an intruder in the room: the screen that detracts from the person before us. As Lynda Chin wrote ‘’Imagine if a doctor can get all the information she needs about a patient in 2 minutes and then spend the next 13 minutes of a 15-minute office visit talking with the patient, instead of spending 13 minutes looking for information and 2 minutes talking with the patient.

“As ubiquitous as it is, AI is not able to solve everything, at least not yet…”

It should not surprise us that technology, despite the dramatic way it has altered our ability to image the body, to measure and monitor its molecular structure, can also fail just as badly as humans fail. The deployment of AI in healthcare comes with a number of risks. While focused, AI may be blind to wider context cues and it may also struggle to deal with the ‘intrinsic uncertainty’ of medicine in the real world. For instance Google’s medical AI was super accurate in a lab but it was a different story in real medical setting.

It’s our chance, perhaps the ultimate one, to bring back real medicine: Presence.Empathy.Trust.Caring.Being Human. That’s the human caring we desperately seek when we’re sick. That’s what AI can help restore. We may never have another shot like this one. Let’s take it .