Every few weeks, a new version of the same anti-AI meme makes the rounds.
A person asks an AI whether a mushroom is edible. The AI confidently says yes. In the next panel, the person is dead, and the AI responds with the now-familiar, blithe apology: “You’re right — that mushroom was poisonous. I’m sorry for the confusion. Would you like to learn more about poisonous mushrooms?”
The meme works because the fear is real – confident wrong answers in high-stakes settings can be dangerous. And yes, bad AI advice can cause harm; that is not in dispute.
But the meme is not analysis. It is an emotional trap. The second panel is the real manipulation. The AI is not merely mistaken, it is portrayed as cheerful, stupid, and morally empty. The reader is invited to feel, not reason. And once that emotional frame is set, the implied conclusion follows easily and ossifies as “AI is unsafe”.
But that conclusion skips the question that matters most.
1. The Hidden Comparator
The mushroom meme compares visible AI failure to invisible human failure, and this is the fallacy embedded in the manipulation. It does not show the person asking a random stranger on the internet. It does not show a Reddit thread full of confident amateurs. It does not show a misread field guide, a family tradition, a foraging influencer, a mislabeled image search, or folk knowledge passed along with too much confidence and too little expertise.
It shows one bad AI answer and silently compares it to an idealized alternative: perfect human judgment.
That is not how safety should be evaluated. In the real world, people already make dangerous decisions with incomplete information. The CDC has warned that wild mushrooms should not be consumed unless identified by an expert, and CDC analysis found that accidental poisonous mushroom ingestion led to an estimated 1,328 emergency department visits and more than 100 hospitalizations in the United States in 2016.
So, yes, an AI system that gives unsafe mushroom advice is a problem, but it is not enough to simply say, “AI can be wrong.” Of course it can. The real question is whether the system being proposed is safer, more reliable, more governable, and more transparent than the alternatives people actually use today.
2. From Mushrooms to Medicine
The same reasoning error appears constantly in healthcare AI. A clinical AI model makes a mistake, or could make a mistake, and the conclusion becomes: “AI is not safe enough for medicine.” Sometimes that concern is appropriate. Sometimes the model is indeed not safe enough. Sometimes the workflow is poorly designed. Sometimes the governance is inadequate. Sometimes the vendor is overclaiming. Sometimes the clinician is overworked. The list is long, and that is precisely the point.
The correct comparator is never an imaginary clinician with infinite time, perfect memory, complete chart context, no fatigue, no interruptions, no documentation burden, no cognitive bias, no productivity pressure, and no missed follow-up.
The correct comparator is the current state of care.
And the current state of care is not perfect. In 1999, the Institute of Medicine’s To Err Is Human estimated that at least 44,000 and perhaps as many as 98,000 people died in U.S. hospitals each year as a result of preventable medical errors. The report also defined medical error as the failure of a planned action to be completed as intended or the use of the wrong plan to achieve an aim.
More recently, diagnostic safety research has estimated that outpatient diagnostic errors affect approximately 12 million U.S. adults each year.
These are not criticisms of clinicians, but it is a recognition of reality. Medicine is complex. Humans are fallible. Workflows are fragmented. Records are incomplete. Time is scarce. Incentives are misaligned. And the systems we have built around clinicians often make it harder, not easier, to deliver safe, consistent, compassionate, quality care.
If the standard is perfection, no human workflow in medicine survives that standard either.
3. Both/And
The most useful framing is neither AI is better than humans nor AI versus humans. That framing encourages exactly the wrong kind of thinking: replacement thinking. It assumes healthcare work can be divided into two teams, human or machine, and that the strategic task is to decide which side wins.
That is not how healthcare transformation works. The better frame is:
- Use AI where AI works best. Use code where code works best. Use people where people work best.
AI is best for work involving scale, ambiguity, summarization, pattern recognition, triage, signal detection, language, synthesis, and probabilistic inference. AI is a powerful interpretive layer that requires context, boundaries, and governance.
Deterministic code is best for hard rules: dose limits, allergy checks, contraindications, identity matching, required fields, escalation triggers, policy constraints, and audit trails. Code should not be “creative.” It should be predictable.
People are best where judgment, accountability, empathy, ethical reasoning, relationship, lived context, and consequential responsibility matter. Humans should not be turned into rubber stamps for machine output. Nor should they be asked to do work that machines or deterministic systems can do better, faster, and more consistently.
Safety does not come from pretending any one of these is infallible. Safety comes from designing workflows that use each where it works best, in a layered, complementary, and redundant safety architecture.
4. The Workflow Is the Safety System
A bad AI workflow can be dangerous; so can a bad code workflow; so can a bad human workflow.
The question is not whether AI (or code or people) can be wrong. The question is whether the system is designed to catch, constrain, and learn from error.
This is where many arguments about healthcare AI go sideways. They focus on the model as if the model is the entire safety system. It is not. The model is one component inside a workflow. The workflow includes inputs, context, constraints, validation, escalation, human review, documentation, accountability, monitoring, and feedback.
FDA’s clinical decision support guidance reflects this logic. The guidance distinguishes certain non-device clinical decision support functions partly by whether they enable healthcare professionals to independently review the basis for recommendations rather than rely primarily on software outputs. It also explicitly discusses automation bias (the tendency to over-rely on automated suggestions) and notes that such bias can produce errors of commission as well as errors of omission.
That matters. A model that provides a recommendation without context, uncertainty, provenance, escalation logic, or reviewability is not the same as a governed human + AI workflow.
The wrong comparator is “AI alone versus perfect clinician.” The right comparator is “current-state workflow versus redesigned human + AI + code workflow.”
5. The Internet Already Taught Us This
Healthcare has seen this movie before.
The internet made vast amounts of medical information available to the public. That was powerful, democratizing … and dangerous. People found high-quality information, but they also found nonsense, conspiracy theories, miracle cures, predatory wellness content, and advice detached from context. The problem was not “information.” The problem was ungoverned information without source quality, personalization, accountability, or appropriate escalation.
Generative AI certainly changes the risk profile because it is interactive, it is fluent, it sounds authoritative, it synthesizes, personalizes, and persuades. That makes the risks different, and in some cases more acute. Which makes the structure of discussion and debate all the more important: not panic, not memes, and not magical thinking about human perfection. Intellectual honesty, critical thinking, genuine curiosity, earnest humility, and elevation of the scientific method in service of the betterment of humanity.
In other words:
- Governance.
- Source discipline.
- Workflow design.
- Human accountability.
- Deterministic guardrails.
- Continuous measurement.
The lesson of the internet is not that people should never use digital information. The lesson is that high-stakes advice requires context, credibility, escalation, and accountability. The same is true for AI.
6. Today’s AI Is Not the Ceiling
There is one more error hidden in many AI safety debates: the assumption that today’s AI is the AI we are evaluating forever – it most definitely is not.
Current models have real limitations. They can hallucinate. They can overgeneralize. They can miss context. They can be brittle when used outside their intended scope. They can amplify bias, launder uncertainty, and produce fluent wrongness. All serious issues for sure.
But we should not evaluate AI against a static ceiling. Models, tools, interfaces, retrieval systems, uncertainty estimation, validation methods, monitoring systems, and governance practices will continue to evolve, improve, and root. That does not mean we should relax safety standards. It means we should evaluate AI as part of an evolving system, not as a frozen artifact. The boundary between what AI can do, what deterministic code should enforce, and what humans must decide will move.
7. From Punchline to Patient Safety
The standard should not be whether AI is perfect. It should be whether a governed human + AI + code workflow is safer, more reliable, more explainable, and more accountable than the current state.
AI can be wrong. Humans can be wrong. Code can be brittle. Workflows can fail. Institutions can normalize harm because the harm is familiar, distributed, and already priced into the status quo.
The goal is not to replace human fallibility with machine fallibility. The goal is to design better systems: systems that use code for deterministic control, AI for scale and synthesis, and people for judgment, accountability, empathy, and ethical responsibility.
The mushroom meme is emotionally satisfying because it turns a real risk into a simple story. But simple stories are not the same as good strategy.
In healthcare, we cannot afford AI hype. We also cannot afford AI fear masquerading as safety reasoning.
We need better comparators, better workflows, better governance, and better questions.
And the first question is simple:
Compared to what?
-Marc d. Paradis
About the Author: Marc d. Paradis’ professional journey is a fusion of academic rigor with real-world impact. He began his career over 30 years ago as an academic molecular neurobiologist, instilling in him a deep respect for critical thinking and the scientific method.
Transitioning into industry, he held leadership roles that bridged data and healthcare: as Vice President of Data Strategy at Northwell Health, Marc leveraged one of the world’s most diverse clinical data sets to drive patient-centered innovation via a $100M partnership with Aegis Ventures, launching multiple AI-centered startups; and as Vice President & Dean of Data Science University at Optum, he spearheaded the training of thousands of professionals in practical, product-centric AI, data-driven decision making, and ethical data practices. In each role, he fostered cultures of curiosity, critical thinking, and collaboration – precursors to the Constructive Inquiry ethos.
About SIYOM Consulting: Founded by Marc d. Paradis, SIYOM Consulting is a boutique advisory specializing in Data and AI Strategy for Healthcare and Life Sciences. We help health-system executives, pharma innovators and investors identify, evaluate and execute on high-value data and AI opportunities.