Hallucinations and the Illusion of Reliable AI

Leading digital transformation across regulated industries, in domains like supply chain, operations, finance, and sales has taught me that risk rarely announces itself.It sneaks in through convenience. Through overconfidence. Through unchecked complexity. And more recently through AI hallucination, which can range from benign to disruptive, loaded with potential liability for high-stakes industries.

While the adoption of generative AI in healthcare, finance, law, and critical infrastructure has been slow, there is more than anecdotal evidence of AI analysis that sounds right, but isn’t. When these guesses get routed into a courtroom, a treatment protocol, or a market forecast, the cost of being wrong is no longer academic. 

AI can be a Critical Vulnerability in Healthcare, Finance, and Law

In 2025, Reuters reported that a U.S. law firm filed a brief including multiple bogus legal citations generated by a chatbot. Seven incidents have already been flagged in U.S courts this year for fabricated case law appearing in pleadings. They all used Generative AI.

In finance, a recent study of financial advisory queries found that ChatGPT answered 35% of questions incorrectly, and one-third of all its responses were outright fabrications.

In healthcare, experts from top universities, including MIT, Harvard, and Johns Hopkins found that leading medical LLMs can misinterpret lab data or generate incorrect but plausible-sounding clinical scenarios at alarmingly high rates. Even if an AI is right most of the time, a small error rate could represent thousands of dangerous mistakes in a hospital system.

Even Lloyd’s of London has introduced a policy to insure against AI “malfunctions or hallucinations” risks, covering legal claims if an under-performing chatbot causes a client to incur damages.

This isn’t margin-of-error stuff. These can be systemic failures in high-stakes domains, often delivered with utmost confidence. The ripple effects of these missteps often extend far beyond immediate losses, threatening both stakeholder confidence and industry standing.

Also Read: The Role of AI in Automated Dental Treatment Planning: From Diagnosis to Prosthetics

Why Hallucinations Persist: The Structural Flaw

LLMs don’t “know” things. They don’t retrieve facts. They predict the next token based on patterns in their training data. That means when faced with ambiguity or missing context, they do what they were built to do: come up with the most statistically likely response, which may be incorrect. This is baked into the architecture. Clever prompting cannot consistently overcome this. And it is difficult, if not impossible, to fix these problems with post-facto guardrails. Our view is that hallucinations will persist whenever LLMs operate in ambiguous or unfamiliar territory, unless there is a fundamental architectural shift away from black box statistical models.

Strategies for Mitigation 

The following rank-ordered list is the steps you could take to limit hallucination.

1. Apply hallucination-free, explainable, symbolic AI to high-risk use cases

This is the only foolproof way to eliminate the risk of hallucination in your high-risk use cases.

2. Limit LLM usage to low-risk arenas
   Not exposing your high-risk use cases to LLMs is also foolproof but does not bring the benefits of AI to those use cases. Use-case gating is non-negotiable. Not all AI belongs in customer-facing settings or mission-critical decisions. Some industries now use LLMs only for internal drafts, never public output — that’s smart governance.
3. Mandatory ‘Human-in-the-Loop’ for critical decisions
   Critical decisions require critical review. Reinforcement Learning from Human Feedback (RLHF) is a start, but enterprise deployments need qualified professionals embedded in both model training and real-time decision checkpoints.
4. Governance
   Integrate AI safety into corporate governance at the outset. Set clear accountability and thresholds. ‘Red team’ the system. Make hallucination rates part of your board-level risk profile. Follow frameworks like NIST’s AI RMF or the FDA’s new AI guidance not because regulation demands it, but because enterprise performance does.

5. Curated, Domain-Specific Data Pipelines
   Don’t train models on the internet. Train them on expertly vetted, up-to-date, domain-specific corpora, e.g. clinical guidelines, peer-reviewed research, regulatory frameworks, internal SOPs. Keeping the AI’s knowledge base narrow and authoritative lowers (not eliminates) the chance it ever guesses outside its scope.
6. Retrieval-Augmented Architectures (not a comprehensive solution)
   Combine them with knowledge graphs and retrieval engines. Hybrid models are the only way to make hallucinations structurally impossible, not just unlikely. 

AI for High-Risk Use Cases

AI can revolutionize healthcare, finance, and law, but only if it can mitigate the risks above and it earns trust through iron‑clad reliability. That means eradicating hallucinations at their source, not papering over symptoms.

There are essentially two options for high-risk use cases given the current state of LLM evolution:

1. Adopt a hybrid solution: hallucination-free, explainable symbolic AI for high-risk use cases, LLMs for everything else.
2. Leave out high-risk use cases, as suggested in #2 above, but that leaves the benefits of the AI unrealized for those use cases. However, the benefits of AI can still be applied to the rest of the organization.

Until there is a guarantee of accuracy and zero-hallucination, AI will not cross the threshold of trust, transparency, and accountability required to find deep adoption in these regulated industries.