On October 30, 1938, Orson Welles delivered one of the most consequential radio broadcasts in American history. His adaptation of War of the Worlds, presented as a series of simulated news bulletins, left listeners in a state of panic. Despite disclaimers and the content’s fictional nature, the format’s realism proved too convincing. Welles had unintentionally exposed a central truth that still haunts us today: when falsehoods are presented through trusted channels, even the most discerning audiences may accept them as real.
Artificial intelligence (AI) now serves as a trusted channel in healthcare. AI systems speak with fluency, structure, and confidence, often integrated into workflows with little visibility. As these tools become more integrated into clinical care and communication, we must confront a critical and growing threat: AI-generated misinformation, also known as hallucinations. These are not minor glitches. They are system-generated responses that are incorrect, misleading, or entirely fabricated—yet delivered with the same confidence as verified clinical guidance. And when this happens in healthcare, the consequences are measured not in public confusion but in patient harm.
Hallucinations Delivered with Authority
AI hallucinations occur when statistical models—trained to generate plausible responses—encounter gaps in data or unfamiliar inputs. Rather than acknowledging uncertainty, these systems often fabricate answers based on pattern-matching logic. A diagnostic tool may identify disease where none exists. A documentation assistant may invent a medical history. A chatbot may offer health advice based on outdated or misapplied evidence. Each of these scenarios can introduce risk into the clinical environment, especially when providers are unaware of the limitations of these tools.
The format itself compounds the challenge. Generative AI models, especially large language models, produce content that appears authoritative. Their outputs are coherent, grammatically correct, and often mirror the tone of professional communication. Yet these systems have no knowledge base in the human sense—no understanding of ethics, clinical nuance, or evidence hierarchies. What they offer is probability, not truth. When clinicians or patients cannot easily distinguish between what has been generated by an AI system and what has been vetted by a human, trust becomes fragile, and outcomes become uncertain.
Transparency Must Be Built In
Trust in healthcare AI must be earned and safeguarded, not assumed. This begins with transparency. Organizations must clearly define and disclose where and how they use AI systems, how they are trained, and how their outputs are verified. Validation reports, update logs, model limitations, and data provenance should be readily available—not just to developers and IT teams but also to clinicians and patients. Any AI system contributing to diagnosis, documentation, or communication must be traceable. Stakeholders must be able to determine whether a recommendation, summary, or interaction originated from a human, an AI, or a combination of both.
Equally important is the ability to correct errors rapidly and systematically. AI hallucinations are not just technical failures; they represent potential breakdowns in care delivery. When identified, they demand swift action to protect patients and analyze what went wrong. Organizations must develop protocols for immediate response, internal reporting, clinician notification, and root cause investigation. Each hallucination, properly analyzed, becomes a data point for model improvement, risk reduction, and governance refinement.
Human Oversight Cannot Be Optional
Clinical oversight remains the final and most vital safeguard. No AI system should replace clinical judgment. Instead, it must augment it—offering suggestions, streamlining tasks, and surfacing insights, all under the control of a qualified provider. Clinicians must be trained to question AI-generated content, identify when an output appears inconsistent or incomplete, and know when to override or disregard AI suggestions. This requires training not just in how to use AI tools but also in how not to use them blindly.
Regulation Is Catching Up—but Slowly
The regulatory environment is beginning to recognize the importance of this issue. The U.S. Food and Drug Administration has proposed frameworks for continuous post-market surveillance of AI-based software as a medical device. The European Union has advanced its Artificial Intelligence Act, which imposes rigorous transparency, documentation, and oversight requirements for high-risk AI tools, including those used in the healthcare sector. These emerging regulations underscore the same core principle: AI in healthcare must be safe, effective, and accountable—before, during, and after deployment.
Still, regulation alone will not eliminate hallucinations. That responsibility lies with the institutions that deploy these systems and the professionals who use them. Organizations must implement governance models that go beyond compliance. Performance monitoring, bias detection, clinician feedback loops, and interdisciplinary review processes are essential components of safe AI deployment. As proposed in a 2024 JAMA article by Shah and colleagues, independent assurance labs offer a credible path forward—validating AI tools before deployment, publishing performance data, and supporting long-term monitoring across healthcare settings.
Precision Brings Power—and Risk
As generative AI continues to evolve, its applications will expand, including its use in documentation, messaging, education, and triage. With each new application, the likelihood of error increases. So does the opportunity for benefit—if implementation is done responsibly. When patients engage with a healthcare system, they should never wonder whether the information they receive is accurate, reliable, and safe. That burden must rest with us.
AI in healthcare should not aspire to be omniscient. It must be humble, transparent, and accountable. Like the fictional aliens in War of the Worlds, hallucinations often arrive unannounced and seem real—until they are confronted by human judgment. Our job is to ensure that the systems we build serve patients, support clinicians, and preserve trust at the heart of healthcare.
Join the Conversation
How is your organization preparing to manage the risks of AI hallucinations in patient care? We value your experiences and insights, and we invite you to share them.
For a deeper dive into the future of AI-driven medicine, order your signed deluxe edition of Future Healthcare 2050 today at BarryChaiken.com/fh2050 or find it in print and ePub editions at Barnes & Noble and Amazon.
Sources:
Shah, N. H., Halamka, J. D., Saria, S., Pencina, M., Tazbaz, T., Tripathi, M., Callahan, A., Hildahl, H., & Anderson, B. (2024). A nationwide network of health AI assurance laboratories. JAMA, 331(3), 245-249. https://doi.org/10.1001/jama.2023.26930
0 Comments