Mark Twain had a talent for locating the soft underbelly of any “miracle” and poking it until the miracle confessed it was
mostly marketing. Drop him into modern health care, hand him a laptop, and introduce him to “medical AI,” and he’d likely
do what he always did: ask impolite questions in plain English, laugh at the fancy words, and follow the money like it left
muddy boot prints across the carpet.
And if today’s medical AI is sometimes flawedoverconfident, under-tested, biased, and occasionally as transparent as a brick
Twain wouldn’t just critique it. He’d dismantle it, piece by piece, with the cheerful precision of a kid taking apart a clock
to see where the ticking lives.
The Twain test: “Explain it so my barber can argue back.”
Twain’s first move would be to drag medical AI out of the chandelier-lit ballroom of buzzwords and into the daylight of the
waiting room. He’d insist on clarity. Not “leveraging advanced machine learning to optimize care pathways,” but:
“What does it do, exactly, and what happens when it’s wrong?”
That demandplain language about function and failuresounds quaint until you realize it’s the foundation of patient safety.
When clinicians can’t tell what an algorithm is trained on, how it performs across different patients, or when it should be ignored,
the technology becomes a polite authority figure. And polite authority figures have a long history of being confidently mistaken.
1) He’d follow the money first, because incentives write the “truth” in pen
Twain would recognize the oldest trick in the modern playbook: when a system is praised as “efficient,” someone is usually
saving time… or saving money… or saving money by saving time. In health care, that can mean fewer clicks for clinicians (good),
faster documentation (good), or faster denials of care (very much not good).
The coverage-decision problem: AI can “assist,” but it must not replace the patient
Some of the sharpest concerns about flawed medical AI show up where patients rarely see them: utilization management,
prior authorization, and coverage determinations. The temptation is obviousif an algorithm can predict what will be approved,
why not let it decide? Twain would call that “letting the cash register practice medicine.”
U.S. guidance has been moving toward a clear principle: algorithms may be used to assist, but decisions still have to be
individualized and consistent with medical necessity standards. In other words, no model gets to override the patient’s
medical record, the clinician’s judgment, or the details that make one human different from the next.
Twain’s point wouldn’t be anti-technology. It would be anti-pretending. If a system’s business model rewards denials,
delays, or friction, it doesn’t matter how poetic the press release isthe incentive will leak into the product like smoke
under a door.
2) He’d ridicule the black box until it either explained itself or admitted it can’t
Twain loved exposing “mystery” that’s really just “nobody’s allowed to ask.” Many medical AI products still arrive with
a gap between what they promise and what they reveal: limited transparency about training data, limited disclosure about
performance across populations, and limited clarity about when the tool should not be used.
Transparency isn’t a vibe; it’s a requirement you can audit
Modern policy is starting to force the conversation into measurable territory. In U.S. health IT regulation, there’s a growing
push for baseline transparency about decision support toolsinformation that helps clinical users assess validity, effectiveness,
safety, and fairness instead of just trusting a vendor’s confidence.
Twain would applaud that shift, then ask the follow-up: “If the information exists, why wasn’t it offered first?”
3) He’d demand evidence like a prosecutorand treat “pilot results” as suspicious relatives
Twain had no patience for claims that couldn’t survive contact with reality. Medical AI often performs beautifully in controlled
settings and then stumbles in real clinicsdifferent patients, different workflows, different devices, different documentation habits,
different everything.
Model drift: the silent saboteur
Health care changes constantly: new clinical guidelines, new coding practices, new populations, new treatment patterns, new lab assays,
even a new way of writing notes. Models trained on yesterday’s data can degrade quietly. That’s not science fiction; it’s statistics.
If you don’t monitor performance over time, you’re treating an algorithm like a statue when it’s really a houseplant.
“Update it safely” is harder than it sounds
Modern regulators have been grappling with a practical question: if AI tools can learn and change, how do you let them improve
without letting them mutate into something unrecognizable? That’s where concepts like predetermined change control plans come in:
a structured, pre-specified approach for what can change, how it will be validated, and how safety and effectiveness will be maintained.
Twain would translate that into a sentence: “If it’s going to change, write down how you’ll keep it from changing into nonsense.”
4) He’d spotlight bias with the enthusiasm of a man discovering the emperor also forgot his shoes
If Twain had a moral hobby, it was puncturing self-congratulation. Medical AI can inherit bias from the data it learns from,
the outcomes it optimizes, and the proxies it uses. Sometimes the model isn’t “racist” in any human sense; it’s simply faithful
to a system that has been unequal for decadesand it reproduces that inequality at machine speed.
A famous lesson: when “cost” pretends to mean “need”
One widely discussed example in health algorithm research involved risk prediction that used health care spending as a proxy for
health needan approach that can underestimate the needs of patients who historically received less care, even when their medical
burden is high. It’s a classic trap: the model optimizes what’s easy to measure, not what’s ethically correct.
Fairness isn’t optional when civil rights law applies
In the U.S., nondiscrimination obligations don’t vanish just because a decision has a neural network inside it. If a covered entity uses
patient-facing decision support tools (including AI) in ways that discriminate, the “the computer did it” defense is about as persuasive
as blaming a parrot for repeating what it was taught.
Twain would not accept “unintended” as an excuse. He’d accept it as an explanationand then demand a fix.
5) He’d insist on governance: “Who is responsible when the robot gets cocky?”
One reason flawed medical AI keeps escaping into the wild is that hospitals and clinics sometimes buy it like a toaster:
plug it in, admire the shine, and assume it won’t set the kitchen on fire. But medical AI isn’t a toaster. It changes decisions,
workload, documentation, and patient trust. It needs governancereal oversight with names, minutes, escalation paths, and stop buttons.
From “innovation theater” to “risk management you can repeat”
Frameworks like the NIST AI Risk Management Framework push organizations to treat AI risk as a lifecycle responsibility:
map the system, measure performance, manage harms, and govern accountability. In health care, that governance has started
to show up in guidance focused on responsible deployment: monitoring, reporting safety events, assessing bias, securing data,
and training users so the tool is used as intended instead of as imagined.
Twain would summarize governance with a grin: “If everyone is responsible, then nobody isand the patient gets the bill.”
6) He’d turn privacy and security into the main plot, not a footnote
Many medical AI tools depend on datalots of it. Notes, imaging, labs, claims, messages, audio recordings, you name it. The more
data flows, the more privacy and cybersecurity matter. And if an AI tool is integrated into core clinical systems, a security lapse
isn’t just an IT problem; it’s a patient safety problem.
That’s why cybersecurity expectationsespecially around electronic protected health informationhave been getting renewed attention,
including proposed updates to U.S. health information security requirements. Twain would call this “locking the door because you built
a more expensive living room.”
The consent problem: patients deserve to know when AI is in the room
Twain also cared about dignity, usually by mocking anyone who tried to take it away. If an AI system is generating documentation,
summarizing a visit, or influencing a treatment plan, patients should not be kept in the dark by default. Transparency isn’t just
paperworkit’s trust maintenance.
7) He’d propose a simple rule: “AI may advise; humans must decide; evidence must exist.”
Twain was allergic to false certainty. The most dangerous medical AI isn’t the one that’s obviously wrong; it’s the one that sounds
calm, coherent, and inevitableespecially when it’s wrong. Large language models can produce plausible text that contains errors,
omissions, or invented details. In clinical documentation, that risk has led researchers to develop ways to measure hallucination and
omission rates and to improve workflows so errors are caught before they fossilize in the chart.
There’s also the problem of manipulation: if a system is vulnerable to prompt injection or other attacks, it can be nudged toward unsafe
outputs. Twain would call that “letting strangers whisper into the doctor’s ear through a keyhole.”
A Twain-approved checklist for medical AI that wants to be trusted
- Purpose in one sentence: What clinical decision support or workflow does it change?
- Evidence that travels: External validation across sites and populations, not just one proud pilot.
- Fairness proof: Document performance across demographic groups; mitigate bias; repeat the checks over time.
- Monitoring plan: Detect model drift, data shifts, and workflow changes that break performance.
- Human override by design: Clear instructions for when to ignore the tooland no punishment for doing so.
- Transparency and documentation: Baseline disclosures so clinicians can evaluate safety and appropriateness.
- Security and privacy: Strong controls for ePHI, vendor oversight, and incident response readiness.
- Accountability map: Named owners, escalation paths, and a process to pause or retire unsafe models.
So how would Twain dismantle flawed medical AI?
He wouldn’t smash it with a hammer. He’d dismantle it with questions.
He’d ask who benefits, how it fails, where it’s biased, what it hides, how it changes, who’s accountable, and whether the evidence
survives real life. He’d insist that “trustworthy AI” means something you can inspect, not something you can chant.
And after he’d taken the whole contraption apart on the workshop floorgears here, wires therehe’d probably rebuild it in a simpler form:
an assistant that’s humble, monitored, auditable, fair, secure, and permanently allergic to pretending it’s the doctor.
Experiences from the front lines: scenes Twain would recognize (and roast)
The following are composite, real-world-flavored scenariosstitched together from commonly reported challenges in health AI adoption.
They’re not tales of villainy; they’re tales of normal people meeting abnormal complexity. Twain would love them because they show
the gap between what we say technology does and what it actually does on a Wednesday afternoon.
Scene 1: The “helpful” triage tool that got confused by a policy change
A hospital rolls out an AI triage assistant to flag high-risk patients for extra follow-up. For a month, it looks brilliant.
Nurses say it catches subtle warning signs. Leadership beams. Then the hospital changes a documentation template and a coding workflow.
Nobody thinks to tell the model.
Slowly, quietly, the alerts become less useful. The tool starts flagging the wrong patientstoo many false alarms, then missed cases.
Staff trust erodes. Some clinicians begin ignoring alerts altogether, which defeats the point. The vendor insists the model is “performing within
expected parameters,” which is a sentence that sounds scientific and means absolutely nothing without numbers, context, and monitoring.
Twain would say: “If your oracle breaks when you rearrange the furniture, it’s not an oracle. It’s a nervous houseguest.”
Scene 2: The documentation assistant that wrote a beautiful sentence… about something that never happened
A clinician tries an ambient documentation tool that drafts visit notes. The prose is smooth. The structure is tidy. The doctor feels ten pounds
lighter. Then a patient reads the after-visit summary and says, “I never told you that. I never had that symptom.”
It turns out the model stitched together plausible clinical language from partial audio and typical patterns. The error is smalluntil it isn’t.
A “minor” invented detail can shape future decisions, influence insurance, and confuse later clinicians. The fix isn’t to ban the tool; it’s to
design a workflow where humans verify, high-risk fields are double-checked, and the system is evaluated with safety metrics instead of vibes.
Twain would grin: “A machine that can write like a doctor can also lie like a poet. Check its work.”
Scene 3: The fairness surprise: “It works great… for some people.”
A clinic deploys an AI-supported clinical decision support feature. Performance looks strong in the overall metrics. Then someone asks a dangerous
question: “How does it perform across different groups?” The answer is complicated. In certain populations, false positives rise. In others, the model
misses too much. The “average” score hid uneven performance.
The team learns an uncomfortable lesson: bias isn’t always a dramatic failure; it can be a quiet skew. The remedy takes workre-evaluating training data,
improving representativeness, adjusting thresholds, validating in new settings, and monitoring over time. But the bigger remedy is cultural: making fairness
checks routine, not reactive.
Twain would call this “the mathematics of polite neglect” and insist that medicine has no business practicing it.
Scene 4: The governance gap: everyone assumed someone else was watching
A health system buys an AI tool through a procurement process that focuses on cost, integration speed, and vendor reputation. The clinical leadership assumes
IT is evaluating safety. IT assumes clinical leadership is evaluating safety. Legal assumes the contract language covers safety. Quality assumes the vendor will
report issues. Meanwhile, the tool is live, influencing decisions, and nobody has a single dashboard showing performance, drift, bias, or error reports.
When an incident occurs, the postmortem reads like a comedy of assumptions. The lesson is simple and boring, which is why it’s powerful: governance must be explicit.
Named owners. Regular review. A process to pause. A place for clinicians to report problems. Metrics that track real outcomesnot just “usage.”
Twain would write: “They installed a watchdog and forgot to feed it information. Then they were surprised it didn’t bark.”
Scene 5: The best-case scenario Twain would actually approve of
A different hospital rolls out medical AI with a responsible playbook: it defines the use case narrowly, validates across sites, publishes performance and limitations,
trains users, monitors drift, audits fairness, secures data, and creates a clear escalation path. The tool saves time and improves consistency, but clinicians remain
firmly in control. Patients are informed in plain language. Problems are reported and fixed.
Twain would still make jokeshe’s Twainbut he’d also recognize the difference between a helpful assistant and a glossy hazard. He’d approve of humility, because
humility is what keeps medicine safe when certainty is tempting.
Conclusion
If Mark Twain were dismantling today’s flawed medical AI, he wouldn’t be anti-innovation. He’d be anti-nonsense.
He’d force medical AI to earn trust with evidence, transparency, fairness, governance, and securitythen he’d keep checking,
because systems drift, incentives distort, and confidence is cheap.
Medical AI can absolutely help clinicians and patients. But Twain would insist it must do so honestly: as a tool that can be tested,
monitored, questioned, andwhen necessaryignored. The goal isn’t to make AI sound like a doctor. The goal is to make it safe enough
that doctors can use it without risking the patient’s wellbeing or their own judgment.
