Could AI make doctors obsolete?

Yes, says Jörg Goldhahn. Artificial intelligence systems will continue to evolve and outperform doctors in many ways earlier than we think.

Jörg Goldhahn

Artificial intelligence (AI) systems simulate human intelligence by learning, reasoning, and self correction. Already this technology shows the potential to be more accurate than physicians at making diagnoses in specialties such as radiology, dermatology, and intensive care; at generating prognostic models; and at performing surgical interventions.1 And in 2017 a robot passed China’s national medical exam, exceeding the minimum required by 96 points.2

AI im Spital
Computer systems could replace physicians, because in many parts of the world the demand for physicians is growing faster than the supply (symbol image). (Photograph: Shutterstock)

More precise, more reliable, more comprehensive

Even if machines are not yet universally better than doctors, the challenge to make them better is technical rather than fundamental because of the near unlimited capacity for data processing and subsequent learning and self correction. This “deep learning” is part of “machine learning,” where systems learn constantly without the potential cultural and institutional difficulties intrinsic to human learning, such as schools of thought or cultural preferences. These systems continually integrate new knowledge and perfect themselves with speed that humans cannot match. Even complex clinical reasoning can be simulated, including ethical and economic concerns.

Increasing amounts of more comprehensive health data from apps, personal monitoring devices, electronic medical records, and social media platforms are being integrated into harmonised systems such as the Swiss Personalised Health Network.3 The aim is to give machines as complete a picture as possible of people’s health over their life and maximum knowledge about their disease.

The notion that today’s physicians could approximate this knowledge by keeping abreast of current medical research while maintaining close contact with their patients is an illusion, not least because of the sheer volume of data. Here too, machines have the advantage: natural language processing enables them to “read” rapidly expanding scientific literature and further teach themselves, for example, about drug interactions.4

"To say that patients always require empathy from human doctors is to elide important differences between patients."Jörg Goldhahn

The key challenges for today’s healthcare systems are economic: costs are rising everywhere. Introducing AI driven systems could be cheaper than hiring and training new staff.5 AI systems are also universally available and can even monitor patients remotely. This is important because demand for doctors in much of the world is growing more quickly than supply.6

Less biased, less unstable, still caring

The ability to form relationships with patients is often portrayed as the trump card in favour of human physicians, but this may also be their Achilles’ heel. Trust is important for patients’ perception of the quality of their care.7 But the object of this trust need not be a human; machines and systems can be more trustworthy if they can be regarded as unbiased and without conflicts of interest.8 Of course, AI systems may be subject to the biases of their designers, but this can be overcome by independent reviews and subsequent iterations.

To say that patients always require empathy from human doctors is to ignore important differences between patients: many, particularly younger, patients with minor complaints simply want an accurate diagnosis and treatment that works.9 In other words: they may rate correct diagnosis higher than empathy or continuity of care. In some very personal situations the services of a robot could help patients avoid feeling shame.

Even patients who crave interaction, such as those with serious or terminal diagnoses, may find that their needs are better met by machines. Recent studies show that conversational agent systems have the potential to track conditions and suggest care10 and can even guide humans through the end of life.11

Doctors as we now know them will become obsolete eventually. In the meantime, we should expect stepwise introduction of AI technology in promising areas, such as image analysis or pattern recognition, followed by proof of concept and demonstration of added value for patients and society. This will lead to broader use of AI in more specialties and, sooner than we think, human doctors will merely assist AI systems. These systems will not be perfect, but they will be constantly perfecting themselves and will outperform human physicians in many ways.

This article was published in the external pageBritish Medical Journal as a part of a pro and con debate. The con part was written by former ETH scientist Vanessa Rampton.

Goldhahn J, Rampton V, Spinas GA: Could artificial intelligence make doctors obsolete? British Medical Journal, 7 November 2018, doi: external page10.1136/bmj.k4563

external pagePodcast of the British Medical Journal with Jörg Goldhahn and Vanessa Rampton

References

1 Sahiner B, Pezeshk A, Hadjiiski LM et al.: Deep learning in medical imaging and radiation therapy. Medical Physics 2018, doi: external page10.1002/mp.13264
2 Yan A: external pageHow a robot passed China’s medical licensing exam. South China Morning Post 2017
3
external pageSwiss Personalized Health Network 2017
4
Lim S, Lee K, Kang J: Drug drug interaction extraction from the literature using a recursive neural network. PLOS One 2018, doi: external page10.1371/journal.pone.0190926
5
Miliard M: external pageHealthcare AI poised for explosive growth, big cost savings. Healthcare IT News 2017
6
IHS Markit: external pageThe Complexity of Physician Supply and Demand: Projections from 2016 to 2030, 2018.
7
Brennan N, Barnes R, Calnan M, Corrigan O, Dieppe P, Entwistle V: Trust in the health-care provider–patient relationship: a systematic mapping review of the evidence base. International Journal for Quality in Health Care, 25: 682, 2013. doi: external page10.1093/intqhc/mzt063.
8
Litvin CB, Ornstein SM, Wessell AM, Nemeth LS, Nietert PJ: Adoption of a clinical decision support system to promote judicious use of antibiotics for acute respiratory infections in primary care. International Journal of Medical Informatics 81: 521, 2012, doi: external page10.1016/j.ijmedinf.2012.03.002
9
Wong C, Harrison C, Britt H, Henderson J: Patient use of the internet for health information. Australian Family Physician, 43: 875-7, 2014
10
Laranjo L et al.: Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc. 2018. doi: external page10.1093/jamia/ocy072
11
Paasche-Orlow M, Bickmore TW: external pageConversational Agents to Improve Quality of Life in Palliative Care

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