When AI Gets the Facts Backward

The failure was not a bug. The AI performed exactly as designed, matching entities (Google, Apple, twenty billion dollars, search) against reliable sources. What it did not do was verify the relationship between those entities—who pays whom. The system excelled at confirming that components existed; it failed to confirm that the direction of the transaction was correctly represented.

Palmer’s response was instructive. He did not abandon AI verification. Instead, he added explicit relational checks to his workflow: detailed examination of claims, direction verification, reverse test of the claim, and automatic failure triggers for any mistake. His conclusion deserves emphasis:

“Subject matter experts must evaluate all agenticoutputs until management is secure in the quality of the results. And even then, evals and workflows must be constantly revised and updated.

“Even the clearest instructions may yield unexpected results.”

If human oversight is essential for verifying a business article about a technology deal, what does that imply for healthcare, where the stakes are not reputational but existential?

The Trust Covenant Demands More

Medicine has always depended on trust—an ancient form of human connection that predates both science and technology. At its heart lies what I have called the trust covenant between caregivers and those who seek their help. It defines the moral architecture of medicine: not a contract written on paper, but a bond inscribed in the conscience of every clinician and the expectation of every patient.

Patients entrust their lives, privacy, and hopes to clinicians, believing each decision is guided by competence, compassion, and integrity. This trust is freely given, not negotiated. It arises instinctively from human vulnerability and the belief that caregivers—and the institutions that support them—will always act in the patient’s best interest.

That covenant now extends to every tool clinicians use, including AI. When a diagnostic algorithm suggests a treatment path, when an ambient scribe summarizes a clinical encounter, when a decision-support system flags a potential drug interaction, the patient assumes these technologies operate within the same ethical framework as the clinician wielding them. The trust is not divisible. If the algorithm errs, the covenant is strained. If the error causes harm, the covenant may be broken.

Palmer’s fact-checking failure involved reversing a financial transaction. In healthcare, the equivalent might be an AI system that correctly identifies a patient’s symptoms, laboratory values, and medical history but misrepresents their relationship—suggesting a treatment contraindicated by a condition the system identified but failed to connect to. The components would be accurate. The conclusion would be both wrong and dangerous.

Why Healthcare Cannot Outsource Vigilance

The lesson from Palmer’s experience is not that AI is unreliable. The lesson is that AI reliability requires human vigilance. This is true in the media and technology industries. It is doubly true in medicine, where the trust covenant imposes obligations that no standalone AI system can currently fulfill.

Clinicians must remain at the center of AI oversight. They interpret findings, recognize limits, and take moral responsibility for outcomes. Continuous validation must become embedded in clinical workflow and as routine as monitoring infection rates or medication interactions. Data-science teams and clinicians must work together to examine performance across populations, identify best practices, and refine outputs to ensure equity and reliability.

Like the electric motor, AI should augment human strength, not replace it. The motor amplifies effort while remaining under human control. We would never allow it to operate without a switch, regulator, or emergency stop button. AI deserves the same boundaries—explicit constraints that ensure it serves its operator.

One of medicine’s greatest strengths is its cultural acceptance of humility. Great physicians routinely say, “I do not know.” These words are not weakness but wisdom, an acknowledgment that uncertainty drives curiosity and collaboration. AI designed with this same humility—an ability to express uncertainty rather than overconfidence—becomes exponentially safer. A model that says, “I do not know enough to answer,” invites human review rather than uncritical acceptance.

From Covenant to Compact

As intelligent systems become embedded in clinical practice, the ancient trust covenant is evolving into what I have described as a trust compact—a shared understanding among clinicians, patients, and technology. This compact does not replace the covenant; it expands it.

Patients are becoming active participants rather than passive recipients of care. AI tools now explain imaging findings, interpret laboratory trends, and help individuals manage chronic conditions. Clinicians gain patients as partners who arrive informed and engaged. AI becomes the translator that connects data to human experience—but only if humans remain responsible for ensuring the translation is accurate.

The compact carries a corollary: if patients are to trust AI-assisted care, they must be confident that someone is watching. Someone must verify that the algorithm’s components and relationships are both correct. Someone must catch the reversed transaction before it becomes a reversed diagnosis.

That someone is the clinician. That someone is the institution. That someone is the governance structure and workflow processes we build around these technologies.

The Path Forward

Palmer closed his account with a phrase that belongs on every healthcare leader’s desk: “I am responsible for the agents I deploy.” In healthcare, that responsibility is not merely professional. It is sacred.

The challenge before us is not whether to adopt AI—that question is settled. The challenge is whether we will build the oversight structures that make AI trustworthy. I have proposed one such structure: the Healthcare AI Review and Transparency (HART) initiative, a public-private partnership that would provide continuous evaluation, transparent reporting, and shared accountability across the industry.

Next week, I will explore recent guidance from the Joint Commission and the Coalition for Health AI, examining what these frameworks accomplish and where they fall short. I will connect their limitations to the HART proposal and draw on lessons from the development of Meaningful Use criteria to illustrate how well-intentioned initiatives can fail when implementation favors expediency over outcomes.

For now, the message is clear: AI requires keeping humans in the loop. Outside healthcare, this is prudent. Inside healthcare, where the trust covenant defines our moral obligation to patients, it is non-negotiable. The tools may be intelligent, but the conscience must remain human.

Sources

1. Palmer, Shelly. “When AI Fact-Checking Gets the Facts Backward.” ShellyPalmer.com, January 25, 2026. https://shellypalmer.com/2026/01/when-ai-fact-checking-gets-the-facts-backward/
2. Chaiken, Barry P. “AI’s 1929 Moment: Building HART for Healthcare Safety.” Future-Primed Healthcare, LinkedIn, 2025. https://www.linkedin.com/pulse/ais-1929-moment-building-hart-healthcare-safety-barry-chaiken-uou3e/
3. Chaiken, Barry P. “Beyond HART: Capitalism’s Safety Net for AI.” Future-Primed Healthcare, LinkedIn, 2025. https://www.linkedin.com/pulse/beyond-hart-capitalisms-safety-net-ai-barry-chaiken-al3ke/