BY PRAVIK SOLANKI AND HANNAH LI

The following piece received 2nd Place in the Writing (Clinical) section of the Auricle Annual Writing Competition 2020.

Three years ago, something incredible happened.

Three years ago, a robot passed China’s medical licensing exam – comprised mostly of patient cases – with flying colours, armed with information absorbed from dozens of textbooks, 400,000 journal articles and 2 million medical records.¹ This seems to refute our conviction that the hard-earned fruit of clinical decision-making can only be attained through years of experience. If robots can learn to outperform us, should we be worried?

The emerging field of Artificial Intelligence (AI) is transforming every industry around the world.² AI stems from the ‘information revolution’ of recent decades – harbouring a world where progress increasingly depends on the efficiency of information processing.³ Granted, this is the next broad chapter in human history after the industrial revolution,⁴ but what if we simply want to preserve our current way of living and working? Sadly, we can no longer ‘hit the breaks’ on our new direction – the global economy now depends on this growth, and besides, nobody knows where the breaks are anymore.⁵ My friend, we are in this for good.

Sci-fi movies notwithstanding, the promise of AI in healthcare seems to lie not in humanoid robots (which remain poorly developed), but rather in machine learning. Machine learning involves the use of automated pattern-seeking algorithms to ‘understand’ datasets through an iterative process.⁶ Much like a child acquiring human language, the algorithm adapts and improves with experience.⁷

Machine learning is already being used for the proactive surveillance of health in ways that helpfully aid clinical medicine. This includes the OUTBREAK project, an Australian initiative employing machine learning on health, environmental and agricultural datasets to predict antimicrobial resistance before it hits our healthcare system.⁸ Another Australian initiative seeks to create the world’s first suicide monitoring system, using machine learning on national ambulance data to identify population patterns and hotspots.⁹ Projects like these attract millions of dollars from funding bodies and herald great promise in supplementing the role of clinicians, who clearly cannot be everywhere to prevent every health emergency imaginable.

In the clinical terrain, the increasing accuracy of machine learning in diagnostic tasks may make some clinicians nervous. One machine learning algorithm, trained on close to 130,000 images of skin lesions, could detect dermatological malignancies as well as 21 dermatologists.¹⁰ Another, trained on a dataset of over 34,000 chest x-rays, could detect malignant pulmonary nodules with an accuracy exceeding 17 out of 18 radiologists in the study.¹¹ Machine learning models are even being developed for low-resource settings such as rural India, where a lack of ophthalmologists currently makes diabetic retinopathy notoriously difficult to screen for.¹² The processing capacity of these algorithms is enormous – a sufficiently trained image-based machine learning algorithm could process a gargantuan 3000 images every second running off a $1000 graphics card.¹³ There’s just no way we could keep up, and we may even be outperformed.

Another CSIRO project aims to diagnose mental disorders through a series of decisions made by participants in a computer game. These algorithms find subtle differences – too subtle for humans to appreciate – that differentiate those with depression, those with bipolar disorder, and those with neither. Although in early stages, this research ultimately aims to replace the ‘subjective’ diagnosis of mood disorders, motivated by the fact that most patients with bipolar disorder are initially misdiagnosed with depression in current clinical practice.¹⁴ Moreover, since a solution like this can be delivered digitally, it could even improve access to healthcare.¹⁵

Soon, in addition to diagnosis, machine learning could also transform the management of diseases. The AI supercomputer IBM Watson can already pour through decades of accumulated data on a patient to generate an accurate problem list (alongside relevant medical literature) in seconds.¹⁶ This aspect of AI could remove the “data clerk role” that every hospital intern would be familiar with – something we should certainly look forward to.¹⁷

However, if we are not careful, the same features of AI that invite progress can just as easily breathe new dangers into existence. The same IBM Watson once suggested treatment with a monoclonal antibody for an oncology patient, without considering what the anticoagulant effect of this drug would have on the patient – who was already bleeding severely.¹⁸ If followed through, this AI-driven decision could have been unimaginably disastrous.

Even if we encode contraindications into algorithms, their internal decision-making process often remains unknown to us humans.¹⁹ This creates a ‘black box’ of complex statistical logic (potentially involving millions of variables) that cannot be explained in a transparent and accountable manner.²⁰ Can we rely upon a decision we cannot explain – and could we defend a decision-gone-wrong on sound ethico-legal grounds?²¹ Uncomfortably, the ‘black box’ nature of algorithms continues to be a challenging hurdle in the safe implementation of AI.²⁰

Moreover, the success of machine learning algorithms relies on two factors – the algorithm itself, and the data it is trained on.²² Where data is lacking, existing disparities in health could be exacerbated. To understand how this could happen, we can look to facial recognition systems. Due to darker-skinned women being under-represented in training data, commercial algorithms have been shown to accurately detect gender with an error of 35% for darker-skinned women, compared to only 0.8% for lighter-skinned men.²³ Since ethnic minorities are less represented in medical datasets than White men,²⁴ similar disparities could emerge when machine learning is applied to healthcare, provoking questions around fairness and justice.

In reality, no amount of AI computation can ever replace the integrity of human values. Conversely, our Mammalian brains cannot rival the exponential progression of AI computational power. Recognising the unique strengths of both parties, the World Medical Association in 2019 stated that AI in healthcare should refer instead to “augmented intelligence” to better reflect the unfolding reality.²⁵ As prominent AI expert and cardiologist Eric Topol puts it, the ultimately goal is for “synergy” between humans and AI.¹⁸

And of course, the empathy and holistic social understanding clinicians bring to the table – that unique ‘human touch’ – is something no machine can ever emulate. As the American physician Francis Peabody once noted, the task of clinicians is to translate “that case of mitral stenosis in the second bed on the left” into “Henry Jones, lying awake [at] night while he worries about his wife and children.”²⁶ Only we can understand Henry Jones as a human being rather than a disease – this ball remains well and truly in our court.

In the 21^st century, clinicians and AI are fostering an evolving symbiotic relationship, with each bringing their unique talents to the table. Our challenge as clinicians will be to maintain our clinical acumen, social values and human empathy as we open The Next Chapter with cautious optimism.

 

References

1. Yan A. How a robot passed China’s medical licensing exam China: South China Morning Post; 2017 [Available from: https://www.scmp.com/news/china/society/article/2120724/how-robot-passed-chinas-medical-licensing-exam.
2. Hajkowicz SA, Karimi S, Wark T, Chen C, Evans M, Rens N, et al. Artificial Intelligence: Solving problems, growing the economy and improving our quality of life. Australia: CSIRO Data61; 2019.
3. Gurría A. From the information revolution to a knowledge-based world: OECD Observer; 2012 [Available from: https://oecdobserver.org/news/fullstory.php/aid/3905/From_the_information_revolution_to_a_knowledge-based_world.html.
4. Harari YN. Sapiens: A Brief History of Humankind. London, UK: Penguin Random House; 2011.
5. Harari YN. Homo Deus: A Brief History of Tomorrow. London, UK: Penguin Random House; 2015.
6. Benke K, Benke G. Artificial Intelligence and Big Data in Public Health. Int J Environ Res Public Health. 2018;15(12).
7. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36-S40.
8. OUTBREAK. How can we prevent antimicrobial resistance? Combining AI and big data to track, trace and tackle AMR Australia: OUTBREAK; 2020 [Available from: https://outbreakproject.com.au/antimicrobial-resistance-solution/.
9. Monash University. Google grant to establish world-first suicide monitoring system Melbourne, Australia: Monash University; 2019 [Available from: https://www.monash.edu/news/articles/google-grant-to-establish-world-first-suicide-surveillance-system.
10. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.
11. Nam JG, Park S, Hwang EJ, Lee JH, Jin KN, Lim KY, et al. Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology. 2019;290(1):218-28.
12. Gulshan V, Rajan RP, Widner K, Wu D, Wubbels P, Rhodes T, et al. Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India. JAMA Ophthalmol. 2019.
13. Beam AL, Kohane IS. Translating Artificial Intelligence Into Clinical Care. JAMA. 2016;316(22):2368-9.
14. Purtill J. Australian researchers design computer game to diagnose depression and bipolar Australia: CSIROscope; 2019 [updated 10 Oct 2019. Available from: https://blog.csiro.au/computer-game-to-diagnose-depression-and-bipolar/.
15. Blashki G. Would you trust AI with your mental health? Pursuit: University of Melbourne; 2019 [Available from: https://pursuit.unimelb.edu.au/articles/would-you-trust-ai-with-your-mental-health.
16. Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer? Am J Med. 2018;131(2):129-33.
17. Hsu J. Artificial Intelligence Could Improve Health Care for All—Unless it Doesn’t: TIME; 2019 [Available from: https://time.com/5650360/artificial-intelligence-health-care/.
18. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
19. Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: Mapping the debate. Big Data & Society. 2016;3(2).
20. Australian Academy of Health and Medical Sciences. Artificial Intelligence in Health: Exploring the Opportunities and Challenges. Report from a Roundtable Meeting. Australian Academy of Health and Medical Sciences; 2020.
21. London AJ. Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. Hastings Cent Rep. 2019;49(1):15-21.
22. Domingos P. A few useful things to know about machine learning. Communications of the ACM. 2012;55(10):78-87.
23. Zou J, Schiebinger L. Design AI so that it’s fair. Nature. 2018;559:324-6.
24. Nordling L. Mind the gap. Nature. 2019;573:S103-5.
25. World Medical Association. WMA Statement on Augmented Intelligence in Medical Care. 70th WMA General Assembly, Georgia; 2019.
26. Peabody FW. The Care of the Patient. JAMA. 1927;88(12):877-82.

 

 

BY ISAAC TANG

“Th[e] accumulation [of the sciences] from age to age is essential. Thanks to it, we are like children on the neck of a giant, as we can see all that the giant sees, and a bit more besides.”

– Guy de Chauliac (c1300-1368)

It is July, 1368 in Lyon. When his student arrives, Guy de Chauliac is sitting at his desk. Summer light, floral fragrances and the chimes of church-bells pour through the open window. He looks up with a smile of recognition creeping across his wizened face, beneath strands of white hair. His voice is soft and trembling, but his greeting is warm. His hands clasp a leather-bound book, with the words Chirurgia Magna partially visible, as if physically protecting the treasures of knowledge he has documented over his life as a surgeon to three popes in Avignon. Then reaching towards the student, he winces in pain and rubs around his shoulder. “Arthritis,” he says apologetically. “But that is also where the bubo was, back in the day.”

“From the plague?” asks the student. De Chauliac nods. After a pause, the student queries cautiously, “What was it like during the height of the plague?”

De Chauliac shivers. “Terrible.” His eyes gaze vacantly out the window, as the internal visions of his mind take over. “We first heard reports of it sweeping through the east after a triple conjunction of Jupiter, Saturn and Mars. Divine punishment for the Turks and Tartars who were advancing with their powerful armies, we thought and gloated – for no-one expected it to crush us too.

“When it reached Avignon, the scale was unimaginable. Death glided through the alleyways, crept up the stairs and seeped beneath the doors. When we physicians encountered failure after failure, we became distressed at our ignorance and helplessness. Have we not read Hippocrates, Galen and Avicenna? Did we not attend prestigious universities and study under eminent professors? Yet when we saw the sick, imploring for help with sunken ghoulish eyes, violent bouts of bloodied coughing, the putrid-smelling gangrene, we hung our heads in shame – we were useless. No amount of herbs, vinegar baths, exotic potions or bloodletting could defend against our invisible foe. Many doctors quickly packed their belongings and fled before they died like their patients.”

“But you didn’t flee,” says the student.

De Chauliac turns and answers coldly. “I was afraid to be called a coward. I stayed and observed the nature of the plague and found two types. One was incredibly contagious and caused intense fevers and haemoptysis. The other was slower but agonising to watch, creating swelling tumours (or buboes) in the groin and armpits. As people fell like cards, I watched the threads of society unravel before me, for the plague unearthed another plague, a plague we have suffered since the Original Sin. Our enmities, our selfishness, our deepest fears were laid bare.

“Families abandoned each other, every man for himself and the sick died alone. The son was

not heard, the daughter not found and the priest absent, too fearful to administer the holy rites one final time. Courageous monks and nuns who initially tended the afflicted were soon no more, for the plague took them too. Others, as if the suffering were not enough, became flagellants and savagely whipped themselves to pulp overrunning with blood. Disregarding ecclesiastical censures and excommunications, oh how they harrowed those witnessing their grisly processions from dawn to dusk! But as the multiplying coffins were carried and marched sombrely through the oppressive night, the weeping that reverberated within the cold city walls gradually metamorphosed into the monstrous shriek of the dragon. People began turning against each other, perpetrating ghastly, horrendous crimes.”

Here, de Chauliac pauses and purses his lips in grief before continuing. “People wanted someone to blame. They hounded beggars and lepers and forced them out the towns. They suddenly found witches and heretics everywhere, deserving to be stabbed, hanged, burned. Pilgrims and friars were dragged through the streets, their robes ripped and soiled. Then people began rounding up the Jews, accusing them of the most outrageous things: poisoning the wells, manufacturing maladies. While they fought and murdered, the plague ploughed through indiscriminately, tearing down the rich, the poor, Catholics and Jews.

“Pope Clement VI, in whose service I was at the time, was appalled to witness this barbarity and chaos as he sat enthroned, almost comedically, between two huge bonfires to purify the surrounding air from pestilence. He published papal decrees calling for the protection of Jews and labelling their persecutors as unwitting followers of the Deceiver. Sadly, the ears of Hatred are deaf and its hands swift to destruction. Widespread massacres, mass burning of Jews, plundering and ransacking of their houses erupted through the continent, bursting like the purulent tumours of the plague.”

As he shudders from the torrent of memories, a sparrow descends and alights on the windowsill between the pots of furry sage and slender thyme, warbling in the green light filtered through the leaves of an apple tree. Calmed by its coloraturas, de Chauliac smiles. “What a beautiful day,” he sighs. “A day like this could erase even the darkest recollections. I once thought I would never live to this age, when I got the plague myself. I teetered at the edge of life and death for weeks but, by the will of God, I escaped – like a bird from the fowler’s snare.”

Wistfully, he muses, “Look at the marigolds unfolding their golden heads. Look at the roses overhanging the window with their deep hue. Whilst we mourn the dead, the earth foretells a renaissance.” He hands his book to the student who flips through the pieces of parchment that come alive with black ink. There are instructions about anaesthesia, wound management, trephination, inguinal hernia repair, treatments for fractures, medical professional standards and, of course, the plague.

“Each successive generation stands on their predecessors like a child on the shoulders of giants. Whilst we only see an imposing mountain now, they will one day see the ocean beyond, an ocean of knowledge and wisdom regarding all kinds of diseases and cures.” He faces his student and wonders aloud, “But will they suppress the second plague, the plague unseen, that lurks within us?”

References

Carmichael AG. Universal and particular: the language of plague, 1348-1500. Med Hist Suppl. 2008(27):17-52.

Getz F. Inventarium sive Chirurgia Magna. Vol. 1 [Internet]. Bulletin of the History of Medicine; 1998 [cited 2020 Apr 28]. Available from: https://muse.jhu.edu/article/4125.

Hajar R. The Air of History (Part II) Medicine in the Middle Ages. Heart Views. 2012;13(4):158-62.

Keys TE. The Plague in Literature. Bull Med Libr Assoc. 1944;32(1):35-56.

Thevenet A. Guy de Chauliac (1300-1370): the “father of surgery”. Ann Vasc Surg. 1993;7(2):208-12.

Watters DA. Guy de Chauliac: pre-eminent surgeon of the Middle Ages. ANZ J Surg. 2013;83(10):730-4.