Medical AI – The Health Care Blog

The Dartboard Toss and the Algorithm

Dec 16, 2025

By GEORGE BEAUREGARD

How A.I. could have personalized my 2005 cancer journey

I don’t think I’m in the minority of Baby Boomer physicians when it comes to my curiosity and ambivalence about the progressing application of A.I. in medicine. But that curiosity isn’t just prospective, it’s retrospective too. In 2005, I became an outlier who perhaps needed something other than the standard of care for a disease.

During the fall of 2005, I first saw a single drop of blood hit the toilet water while I was urinating in my bathroom. After hitting the water, the rose-colored bead slowly sank, twisting and contorting, dissipating like a puff of smoke. The evidence was fleeting—gone in seconds. If I were a spectator rather than the source, I might have admired its visual artistry. There was no associated pain.

A single thought ran through my mind: Did I just pee blood? I thought I had perhaps imagined it.

I was 49 years old and didn’t have what were considered risk factors for kidney or bladder cancer: smoking, obesity, advanced age, high blood pressure, or exposures to cadmium, trichloroethylene, or herbicides. But I was adopted and lacked any knowledge whatsoever about my family history. Did I have a grim genealogy? What was perhaps significant, however, was that both of my adoptive parents had developed different types of urogenital cancer. That led me to speculate that environmental factors related to materials in our house and/or the land it sat on or around it had perhaps played a role.

I tried to dismiss any concerns, but the adage “painless hematuria is cancer until proven otherwise” ran through my mind in chyron-like fashion.

The episodes continued and worsened, prompting an ultrasound, the report of which read: “…a soft tissue density is seen in the base of the bladder toward the right. While this could represent thrombus, I cannot rule out a primary mucosal lesion. The lesion measures approximately 4 X 5 cm in diameter.”

I consulted a urologist colleague, who performed a cystoscopy. His comment about what he saw: “As you know, you have a mass in your bladder. I got a very good view of it. It’s pretty angry-looking, so I suspect it’s not benign. I tried to remove as much as I could. It would’ve been pretty risky to scrape deeper and risk puncturing your bladder. I know I didn’t get all of it.” A TURBT soon followed. The pathology showed a high-grade urothelial carcinoma extensively invading the lamina propria and muscularis propria. There was multifocal lymphovascular invasion, so I probably had a more advanced subgroup than the localized SEER stage.

At that time, the relative five-year survival rate for stage II muscle-invasive bladder cancer was about 45 percent.

Overwhelmingly, bladder cancer is an age-related malignancy. So, there I was, 49 years old, with a cancer whose median age of incidence—septuagenarians— was much older than mine. A WTF moment.

One that started me thinking about how much time I had left.

So, I had cancer, but in some ways felt cautiously optimistic. I had access to Boston-based academic centers and specialist colleagues who were willing to see me quickly, and good insurance.

But getting the diagnosis was only the beginning. I saw three expert urologists, each of whom recommended a radical cystectomy, small bowel resection, and construction of an orthotopic ileal neobladder. Convergence. Certainty for me.

Will Artificial Intelligence (AI) Trigger Universal Health Care in America? What do expert Academics say?

Apr 18, 2024

By MIKE MAGEE

In his book, “The Age of Diminished Expectations” (MIT Press/1994), Nobel Prize winner, Paul Krugman, famously wrote, “Productivity isn’t everything, but in the long run it is almost everything.”

A year earlier, psychologist Karl E. Weich from the University of Michigan penned the term “sensemaking” based on his belief that the human mind was in fact the engine of productivity, and functioned like a biological computer which “receives input, processes the information, and delivers an output.”

But comparing the human brain to a computer was not exactly a complement back then. For example, 1n 1994, Krugman’s MIT colleague, economist Erik Brynjolfsson coined the term “Productivity Paradox” stating “An important question that has been debated for almost a decade is whether computers contribute to productivity growth.”

Now three decades later, both Krugman (via MIT to Princeton to CCNY) and Brynjolfsson (via Harvard to MIT to Stanford Institute for Human-Centered AI) remain in the center of the generative AI debate, as they serve together as research associates at the National Bureau of Economic Research (NBER) and attempt to “make sense” of our most recent scientific and technologic breakthroughs.

Not surprisingly, Medical AI (mAI), has been front and center. In November, 2023, Brynjolfsson teamed up with fellow West Coaster, Robert M. Wachter, on a JAMA Opinion piece titled “Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?”

Dr. Wachter, the Chair of Medicine at UC San Francisco, coined his own ground-breaking term in 1996 – “hospitalist.” Considered the father of the field, he has long had an interest in the interface between computers and institutions of health care.

In his 2014 New York Times bestseller, “The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age” he wrote, “We need to recognize that computers in healthcare don’t simply replace my doctor’s scrawl with Helvetica 12. Instead, they transform the work, the people who do it, and their relationships with each other and with patients.”

What Brynjolfsson and Wachter share in common is a sense of humility and realism when it comes to the history of systemic underperformance at the intersection of technology and health care.

They begin their 2023 JAMA commentary this way, “History has shown that general purpose technologies often fail to deliver their promised benefits for many years (‘the productivity paradox of information technology’). Health care has several attributes that make the successful deployment of new technologies even more difficult than in other industries; these have challenged prior efforts to implement AI and electronic health records.”

And yet, they are optimistic this time around.