“ARTIFICIAL intelligence [AI] could put doctors and lawyers OUT of a job in FIVE YEARS’ time,” trumpeted a headline in Britain’s Express earlier this year.

Could it be true?

There’s no doubt technology is radically changing the nature of work across most areas of human endeavour.

One of the many challenges confronting us as a society is the question of how to distribute meaningful employment, and the wealth associated with it, in a world where we just won’t need as much human labour as we used to.

We often assume that the jobs that will be lost will be mostly unskilled, but perhaps we’re kidding ourselves.

As machines become more “intelligent”, able not just to follow the rules we’ve given them but to continually improve their performance by learning from mistakes, they will inevitably expand their reach into new areas.

Intelligent systems are being developed that may match or even outperform radiologists in examining brain scans for stroke risk, or pathologists in assessing biopsies.

While that might be worrying news for the next generation of diagnostic specialists, it does offer hope for greater equality in access to health services (to diagnosis at least, if not to resulting treatment).

Intelligent machines will be able to diagnose far more quickly than any human, potentially processing thousands of images a day and making early diagnosis available to anybody with a smart phone.

A group of Stanford computer scientists and medical experts published an article in Nature this year that showed that their AI system matched dermatologists in its ability to distinguish both melanomas from benign naevi and keratinocyte carcinomas from benign keratoses.

Another study found that a machine-learning algorithm was able to distinguish subtypes of non-small cell lung carcinoma with similar accuracy to that of expert pulmonary pathologists. Combining the predictions of the algorithm with those of the humans led to an 85% reduction in error in detecting metastatic breast cancer in lymph nodes.

Perhaps that’s where AI has the greatest contribution to make, not in replacing humans but in augmenting them.

That may be how previous technological advances have operated, though some believe the disruptions likely to be caused by AI are in a league of their own.

Physicist Stephen Hawking, for example, famously warned that the development of thinking machines could spell the end of the human race.

Even if you don’t buy such doomsday scenarios, it’s worth asking what we may lose in harnessing the undoubted power of the machine for medical diagnosis.

With every new technology come fears of our dependency and resulting loss of our cognitive skills. Calculators took away our ability to do mental arithmetic. Google has destroyed our memories. Spell check made us forget the letters in algorithm.

Interestingly, one of the computer scientists in the Stanford dermatology study, Professor Sebastian Thrun, has also been one of the brains behind Google’s effort to develop driverless cars — an initiative that could, despite its obvious benefits, raise similar concerns.

When Google filmed “drivers” using its test cars, it found that they overtrusted the technology. Despite moving at high speed on the freeway in what they had been told was only a prototype, drivers were observed playing with their electronic devices without even a glance at what was happening through the windscreen.

Could the harnessing of AI in medical diagnosis lead to similar complacency?

Oncologist and author Dr Siddhartha Mukherjee has examined the likely benefits and risks of such developments in a thought-provoking essay in the New Yorker.

The algorithms may be better than humans at identifying pathology, but they would not have our capacity for enquiry, he writes.

“… in my own field, oncology, I couldn’t help noticing how often advances were made by skilled practitioners who were also curious and penetrating researchers.

“The chain of discovery can begin in the clinic. If more and more clinical practice were relegated to increasingly opaque learning machines, if the daily, spontaneous intimacy between implicit and explicit forms of knowledge … began to fade, is it possible that we’d get better at doing what we do but less able to reconceive what we ought to be doing, to think outside the algorithmic black box?”

It’s a good question. Whatever the future holds, it’s certainly going to be interesting.

Jane McCredie is a Sydney-based science and health writer.

 

To find a doctor, or a job, to use GP Desktop and Doctors Health, book and track your CPD, and buy textbooks and guidelines, visit doctorportal.

 

 

3 thoughts on “Artificial intelligence: augmenting or replacing doctors?

  1. Sue Ieraci says:

    Thanks for the article, Jane. In my view, technology is a great substitute for human memory, but not for human judgement. Experienced clinicians operate at a level of sophistication above protocols, since health care is a human service. The further away from my initial training I get, and the more new research evidence is produced, the more I value the techonological solutions that allow me to search – for anything – instantly, rather than try to hold things in my head. I have no concern about loss of skills, because I see my skills as problem-solving at a human level, not retaining facts in my memory.

    I am aware of various projects that sought to reduce medical practice into a series of algorithms. The real skill that was missing was picking which patients to place on which algorithm, and when to exit them. Patients need validation of their symptoms, an explanation of what is going on (the pathophysiology) and a plan for managing. Technology can be a very useful tool along the way, but not a substitute.

  2. Bruce says:

    I’m not sure that you understand what AI is doing in these algorithms. It is not only learning what has gone before through the data that you give it. The deep neural networks have the capacity to actively learn. An AI system for example looking at CT images does not look at the images……it lays out each pixel from each image and can detect single differences. Previously, the limitations of AI were the computing power and now that is catching up. Not only is it GOING to happen…it is happening all around you. Let us not allow the familiar arrogance and resistance to change in medicine, to slow down the advance of this science within medicine. It certainly can augment those who are experienced and highly resourced…….for those who are isolated and not so affluent, such systems can provide evidence based, highly evolved and accurate diagnostic and therapeutic tools. The future is already here.

  3. Sue Ieraci says:

    Do you recognise the difference between a machine analysing CT images vs an experienced clinician analysing complex human situations and making judgement decisions?

    I don’t see anyone expressing what you refer to as ” the familiar arrogance and resistance to change in medicine” – we embrace technology readily. Within my own clinical lifespan, we have gone from basic CTs of a few slices, to helical CT with reconstructions, MRI in daily practice, ultrasound in daily practice and electronic medical records. Here we are, using social media.

    The type of intelligence that can analyse digital images is not the same as one that can interact with a fellow human being.

    If proponents of AI understood this, they would promote the use of AI as a tool of clinical practice, not a substitute for the clinician.

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