Artificial intelligence could increase job loss. General practitioners’ (GPs) ability to sort through patients’ medical data and diagnose illnesses pales in comparison to the speed, efficiency and accuracy with which computers can perform the same task. Beyond this profession, the most at-risk jobs lie in radiology, dermatology and pathology, whose work is predicated even more-so on data and pattern recognition. Nick Bryan, the department chair of diagnostic medicine at the University of Texas at Austin, argued in a interview, “I predict that within 10 years no medical imaging study will be reviewed by a radiologist until it has been pre-analyzed by a machine.”
Yet, in many of these cases, job loss is not a foregone conclusion. Bryan and his colleague, Michael Recht, wrote an article for the Journal of the American College of Radiology which stated, “We believe that machine learning and AI will enhance both the value and the professional satisfaction of radiologists,” rather than supplant radiologists entirely, because AI will allow “us to spend more time performing functions that add value and influence patient care and less time doing rote tasks that we neither enjoy nor perform as well as machines.”
The market for empathetic, “people-focused” practitioners is large, which will protect the medical labor market even as A.I grows. The idea of the need for truly human-centric care has seeped into the health sphere in recent years as research has developed discussing the health benefits derived from genuine patient-clinician interaction. In his book, Emotional Intelligence, Daniel Goleman argues “many patients can benefit measurably when their psychological needs are attended to along with their purely medical ones.” To support his claim, he cites a study performed on elderly patients with hip fracture at the Mt. Sinai School of Medicine. The study concluded “patients who received therapy for depression in addition to normal orthopedic care left the hospital an average of two days earlier; total savings for the hundred or so patients was $97,361 in medical costs.”
While it is clear the market for empathetic care exists, it remains unfilled. In an article published by the National Center for Biotechnology Information, titled “Time Allocation in Primary Care Office Visits,” the average duration of office visits in the United States hovers at about 17.4 minutes, with the median at about 15.7. Even worse, physicians must spend much of this time performing data entry and reading medical records, causing actual talk time between patients and physicians to total just over 10 minutes. Consequently, many patients reported having felt rushed during their visit, and many practitioners are admitting feeling burnt-out. Fortunately, improved A.I technology will be able to alleviate these problems by conducting the bulk of data digestion and analysis for the doctors, which will allow clinicians to focus on the patients themselves.
This unfulfilled market for empathetic, patient-focused care suggests that artificial intelligence will not serve simply as a complete replacement for all health professionals; instead, it will eliminate the minutiae of rote tasks and allow professionals to truly connect with their patients. As venture-capitalist Kai-fu Lee argues in his book AI Superpowers, “[A.I] lets all doctors and nurses focus on the human tasks that no machine can do: making patients feel cared for and consoling them when the diagnosis isn’t bright.” Discussing radiologists in particular, Eric Topol asserts in his book Deep Medicine, “It will be the radiologist who… is best positioned to communicate results to patients and provide guidance for how to respond to them.” Overall, much of the job disruption artificial intelligence will initiate within the health sector entails changes in job descriptions rather than the familiar cycle of job loss and reinvention typically observed in periods of creative destruction.
Moreover, this emphasis on quality, patient-driven care holds numerous other economic ramifications. It will lead to reduced healthcare costs. Not only will more attentive care decrease medical bills, but professionals will be more cost efficient by focusing on valuable tasks. More careful diagnoses, for example, will trim the burgeoning issue of misdiagnoses and unnecessary prescriptions. The already fast-growing biotechnology sector will only accelerate. According to Statista, from 2012 to 2016, revenues in the biotech industry increased from $89.7 billion to $139.4 billion, and the number of public companies from 602 to 704. With the culture shift to personal care entailed by artificial intelligence’s entrance into traditional medicine, as well as by the capabilities of artificial intelligence itself, this industry will only expand in the future.
Overall, a shift to what Goleman labeled “medicine that cares” will do wonders for all participants in the health sector. That being said, A.I remains limited in its capabilities. The IBM Watson MD Anderson debacle demonstrates these limitations. The company lost $62 million in a failed attempt to turn Watson onto curing cancer. A.I remains in his nascent stages, and it will be some time before the healthcare industry experiences true disruption at its hands. However, its future capabilities are endless, and while disruption will always mean discomfort and some losses, A.I’s potential to catalyze a return to “medicine that cares” will do wonders for healthcare economics at large.