Artificial Intelligence Needs Doctors As Much As They Need It

In this futuristic medical concept, a doctor assesses a patient with robust machine assistance.

(© Elnur/Fotolia)

The media loves to hype concerns about artificial intelligence: What if machines become super-intelligent and self-aware? How will humanity compete and survive? But artificial intelligence today is a far cry from a robot takeover. "AI" is a catch-all term that often refers to machine training or machine learning: There is an abundance of data, vastly more than the human mind can assimilate, being tagged, captured and stored. This systematic data processing requires methodologies that can put it in usable form and formats. While these new developments stoke fear in some corners, the ability to predict outcomes is generally seen as a good thing, as it can mitigate risks and even save lives.

We, collectively, want AI even though it is seldom expressed this way.

The prospects and attempts toward artificial intelligence has been with us for decades. Only recently have the underlying technologies and infrastructure--including computer processing, storage, networking speed and advanced software platforms--become omnipresent. These technological advances enabled the implementation of data mining concepts and the subsequent advantages that were not feasible just a decade ago.

AI is fantastical by vision, evolutionary by experience, and disruptive upon reflection. In the world of health care, AI is already transforming research and clinical practice. We, collectively, want AI even though it is seldom expressed this way. What we, the patient population, patient advocates and caregivers, agree on and want is: (1) timely, precise and inexpensive diagnoses of our ailments, injuries and disorders; (2) timely, personalized, highly effective and efficient courses of therapies; and (3) expedited recovery with minimum deficits, complications and recurrence.

"Artificial intelligence and machine learning will impact healthcare as profoundly as the discovery of the microscope."

Implicitly, we all are saying that we want our healthcare systems and clinicians to accomplish truly inhuman feats: to incorporate all sources of structured data (such as published statistics and reports) and unstructured data (including news articles, conversational analysis by care givers, nuances of similar cases, talks at professional societies); to analyze the data sourced and uncover patterns, reveal side effects, define probable success and outcomes; and to present the best personalized course of treatment for the patient that addresses the ailment and mitigates associated risks. It is hard to argue against any of this.

In a recent published interview, Keith J. Dreyer, executive director of the Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science, says that "artificial intelligence and machine learning will impact healthcare as profoundly as the discovery of the microscope."

But as AI helps physicians in profound ways, like detecting subtle lesions on scans or distinguishing the symptoms of a stroke from a brain tumor, we humans can't get too complacent. Evolving AI platforms will provide more sophisticated sets of "tools" to address both mundane and complex medical challenges, albeit with humans very much in the mix and routinely at the helm.

Humans do not appear endangered to be replaced anytime soon.

Human beings are capable of a level of nuance and contextual understanding of complex medical scenarios and, consequently, do not appear endangered to be replaced anytime soon. These platforms will do some heavy lifting for sure and provide considerable assistance across the healthcare industry. But human involvement is crucial, as we are best at adaptive learning, cognition, ensuring accuracy of the data, and continually providing feedback to improve the machine learning components of the AI platforms that the health industry will increasingly rely upon.

The human/machine interface is not binary; there is no line in the sand. It is fuzzy and evolutionary, a synchronicity that we all will surely witness and experience. In the future, it may be possible that all recorded knowledge, including genetic, genomic and laboratory data, from structured and unstructured sources, can be at the fingertips of your clinician, and then factored into diagnosing your condition and prescribing your course of treatment. This is precision and personalized medicine on a grand scale applied at the micro level--you!

But none of this will diminish the importance of doctors, nurses and all assortment of care providers. Though they all will undoubtedly become more effective with such awesome AI assistance, their job will always be to heal you with compassion, wisdom, and kindness, for the essence of humanity cannot be automated.

Steven Haley
Steven Haley is a tech industry veteran and prolific angel investor. He is highly engaged at the leading edge of innovations through his company affiliations and in multiple capacities, which include advisor, operational roles, committee, and board member. He began his technology career working Numerically Controlled Systems (NC Machines), macro-assembler coding, applications hosted on mainframes and minicomputers, and broadband networking. Present-day initiatives relate to commercialization of software platforms. He has been involved in the healthcare sector for two decades serving on academic hospital boards, technology initiatives, and a medical investment advisory committee for a healthcare VC. He is also involved in numerous medical philanthropic activities, including establishing The BrainScience Foundation. His interest lie in adaptive learning software platforms, analytics, and the applications they support in healthcare, STEM education and enterprises.
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David Kurtz making DNA sequencing libraries in his lab.

Photo credit: Florian Scherer

When David M. Kurtz was doing his clinical fellowship at Stanford University Medical Center in 2009, specializing in lymphoma treatments, he found himself grappling with a question no one could answer. A typical regimen for these blood cancers prescribed six cycles of chemotherapy, but no one knew why. "The number seemed to be drawn out of a hat," Kurtz says. Some patients felt much better after just two doses, but had to endure the toxic effects of the entire course. For some elderly patients, the side effects of chemo are so harsh, they alone can kill. Others appeared to be cancer-free on the CT scans after the requisite six but then succumbed to it months later.

"Anecdotally, one patient decided to stop therapy after one dose because he felt it was so toxic that he opted for hospice instead," says Kurtz, now an oncologist at the center. "Five years down the road, he was alive and well. For him, just one dose was enough." Others would return for their one-year check up and find that their tumors grew back. Kurtz felt that while CT scans and MRIs were powerful tools, they weren't perfect ones. They couldn't tell him if there were any cancer cells left, stealthily waiting to germinate again. The scans only showed the tumor once it was back.

Blood cancers claim about 68,000 people a year, with a new diagnosis made about every three minutes, according to the Leukemia Research Foundation. For patients with B-cell lymphoma, which Kurtz focuses on, the survival chances are better than for some others. About 60 percent are cured, but the remaining 40 percent will relapse—possibly because they will have a negative CT scan, but still harbor malignant cells. "You can't see this on imaging," says Michael Green, who also treats blood cancers at University of Texas MD Anderson Medical Center.

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Lina Zeldovich
Lina Zeldovich has written about science, medicine and technology for Scientific American, Reader’s Digest, Mosaic Science and other publications. She’s an alumna of Columbia University School of Journalism and the author of the upcoming book, The Other Dark Matter: The Science and Business of Turning Waste into Wealth, from Chicago University Press. You can find her on and @linazeldovich.

Reporter Michaela Haas takes Aptera's Sol car out for a test drive in San Diego, Calif.

Courtesy Haas

The white two-seater car that rolls down the street in the Sorrento Valley of San Diego looks like a futuristic batmobile, with its long aerodynamic tail and curved underbelly. Called 'Sol' (Spanish for "sun"), it runs solely on solar and could be the future of green cars. Its maker, the California startup Aptera, has announced the production of Sol, the world's first mass-produced solar vehicle, by the end of this year. Aptera co-founder Chris Anthony points to the sky as he says, "On this sunny California day, there is ample fuel. You never need to charge the car."

If you live in a sunny state like California or Florida, you might never need to plug in the streamlined Sol because the solar panels recharge while driving and parked. Its 60-mile range is more than the average commuter needs. For cloudy weather, battery packs can be recharged electronically for a range of up to 1,000 miles. The ultra-aerodynamic shape made of lightweight materials such as carbon, Kevlar, and hemp makes the Sol four times more energy-efficient than a Tesla, according to Aptera. "The material is seven times stronger than steel and even survives hail or an angry ex-girlfriend," Anthony promises.

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Michaela Haas
Michaela Haas, PhD, is an award-winning reporter and author, most recently of Bouncing Forward: The Art and Science of Cultivating Resilience (Atria). Her work has been published in the New York Times, Mother Jones, the Huffington Post, and numerous other media. Find her at and Twitter @MichaelaHaas!