As a technologist and entrepreneur who has spent decades architecting enterprise-grade AI systems across highly regulated industries, I’ve seen firsthand the chasm between AI’s promise and its practical risks, especially in domains like healthcare, where trust is not optional and the margin for error is razor-thin. Nowhere is the cost of a hallucinated answer higher than at a patient’s bedside.
When an AI system confidently presents false information—whether in clinical decision support, documentation, or diagnostics—the consequences can be immediate and irreversible. As AI becomes more embedded in care delivery, healthcare leaders must move beyond the hype and confront a difficult truth: not all AI is ‘fit for purpose’. And unless we redesign these systems from the ground up—with verifiability, traceability, and zero-hallucination as defaults—we risk doing more harm than good.
Hallucinations: A Hidden Threat in Plain Sight
And yet, there is no doubt that Large language models (LLMs) have opened new frontiers for healthcare, enabling everything from patient triage to administrative automation. But they come with an underestimated flaw: hallucinations. These are fabricated outputs—statements delivered with confidence, with no factual basis.
The risks are not theoretical. In a widely cited study, ChatGPT produced convincing but entirely fictitious PubMed citations on genetic conditions. Stanford researchers found that even retrieval-augmented models like GPT-4 with internet access made unsupported clinical assertions in nearly one-third of cases. The consequences? Misdiagnoses, incorrect treatment recommendations, or flawed documentation.
Healthcare, more than any other domain, cannot afford these failures. As ECRI recently noted in naming poor AI governance among its top patient safety concerns, unverified outputs in clinical contexts may lead to injury or death, not just inefficiency.
Redefining the Architecture of Trustworthy AI
Building AI systems for environments where human lives are at stake demands an architectural shift—away from generalized, probabilistic models and toward systems engineered for precision, provenance, and accountability.
This shift in my view rests on five foundational pillars:
- “Explainability” and Transparency
AI outputs in healthcare settings must be understandable not just to engineers but to clinicians and patients. When a model suggests a diagnosis, it must also explain how it reached that conclusion, highlighting the relevant clinical factors or reference materials. Without this, trust cannot exist.
The FDA has repeatedly emphasized that explainability is essential to patient-centered AI. It’s not just a compliance feature—it’s a safeguard.
(b) Source Traceability and Grounding
Every output in a clinical AI system should be traceable to a verified, high-integrity source: peer-reviewed literature, certified medical databases, or the patient’s structured records. In systems we’ve designed, answers are never generated in isolation; they are grounded in curated, auditable knowledge—every claim backed by a source you can inspect. This kind of design is the most effective antidote to hallucinations.
(c) Privacy by Design
In healthcare, compliance is not an option, it is a necessity. Every component of an AI system must be HIPAA-aware, with end-to-end encryption, stringent access controls, and de-identification practices baked in. This is why leaders must demand more than just privacy policies—they need provable, system-level safeguards that stand up to regulatory scrutiny.
(d) Auditability and Continuous Validation
AI models must log every input and output, every version change, and every downstream impact. Just as clinical labs are audited, so too should AI tools be monitored for accuracy drift, adverse events, or unexpected outcomes. This is not just about defending decisions—it’s also about improving them over time.
(e) Human Oversight and Organizational Governance
No AI should be deployed in a vacuum. Multidisciplinary oversight—combining clinical, technical, legal, and operational leadership—is essential. This isn’t about bureaucracy; it’s about responsible governance. Institutions should formalize approval workflows, set thresholds for human review, and continuously evaluate AI’s real-world performance.
An Executive Framework for Responsible AI Adoption
For healthcare executives, the path forward with AI models should begin with questions. This can include, Is this model explainable, and to which practitioners or audience? Can every output be tied to a trusted, inspectable source? Does it meet HIPAA and broader ethical standards for data use? Can its behavior be audited, interrogated, and improved over time? Who is responsible for its decisions, and who is accountable when it fails?
These questions should also be embedded into procurement frameworks, vendor assessments, and internal deployment protocols. Stakeholders in the healthcare ecosystem can start with low-risk applications, like administrative documentation or patient engagement, but design with future clinical use in mind. They should insist on solutions that are intentionally designed for zero hallucination, rather than retrofitted for it.
And most importantly, any AI integration should involve investments in clinician education and involvement. AI that operates without clinical context is not only ineffective—it is dangerous.
From Possibility to Precision
It’s clear to me that the age of ‘speculative AI’ in healthcare is ending. What comes next must be defined by rigor, restraint, and responsibility. We do not need more tools that impress—we need responsible systems that can be trusted.
Enterprises in healthcare should reject models that treat hallucination as an acceptable side effect. Instead, they should look to systems purpose-built for high-stakes environments, where every output is explainable, every answer traceable, and every design choice made with the patient in mind.
In summary, if the cost of being wrong is high, as it certainly is in healthcare systems, your AI system should never be a cause or reason.
About Dr. Venkat Srinivasan, Ph.D
Dr. Venkat Srinivasan, PhD, is Founder & Chair of Gyan AI and a technologist with decades of experience in enterprise AI and healthcare. Gyan is a fundamentally new AI architecture built for Enterprises with low or zero tolerance for hallucinations, IP risks, or energy-hungry models. Where trust, precision, and accountability are important, Gyan ensures every insight is explainable, traceable to reliable sources, with full data privacy at its core.
