For the past few years, healthcare has been focused on a fundamental question: can we trust AI? Clinicians won’t rely on a black box that is occasionally and unpredictably wrong, and they shouldn’t have to. At the same time, they also won’t accept a system so rigid that it can’t understand a real conversation or a patient’s complex history. The challenge is balancing both.

Modern clinical AI is moving beyond documentation. These systems can summarize patient histories, surface insights across records and identify potential gaps in care. As the impact of these outputs increases, so does the need for reliability. The key isn’t choosing between AI and rules. It’s combining them.

As the impact of these outputs increases, so does the need for reliability. The key isn’t choosing between AI and rules. It’s combining them.

Why LLMs hallucinate

Large language models (LLMs) generate text by predicting the most likely next word based on patterns learned during training. They’re exceptionally good at producing fluent, confident language, but fluency is not accuracy.

Hallucinations generally stem from three issues:

• Incomplete or noisy data: An unclear transcript or ambiguous note can lead the model to fill gaps with plausible but incorrect information.
• A drive to complete the task: Faced with missing details, models often generate an answer rather than acknowledge uncertainty.
• No built-in certainty check: LLMs do not inherently distinguish between what they know and what they’re inferring.

Better data, prompting and model design can reduce hallucinations, but they can’t eliminate them entirely. In healthcare “usually right” isn’t enough.

What determinism brings

Deterministic systems work differently. Rules, lookup tables, validation checks and predefined algorithms produce the same result every time. That’s their strength. Deterministic systems can reject invalid drug codes, flag overdue screenings, catch out-of-range vital signs and enforce required formats and units.

When something must be guaranteed, determinism provides that guarantee.

Its weakness is that it’s literal. Rules can’t easily interpret clinical nuance, understand paraphrased language or make sense of variability found in real-world records. A system built entirely on rules quickly becomes too rigid to be practical.

Why the hybrid model works

The strengths of LLMs and deterministic systems are complementary. A pure LLM can understand conversations and clinical context, but it cannot guarantee it won’t invent information. A pure rules engine is reliable, but it lacks the flexibility to understand the broader clinical picture. The solution is to let each technology do what it does best.

LLMs handle language, summarization and pattern recognition. Deterministic controls own safety-critical functions such as validating values against reference ranges, enforcing units, checking medications and codes against authoritative databases, and confirming patient identifiers. The result is AI flexibility backed by predictable safeguards. If the AI is unsure, it doesn’t guess. It flags the issue for the clinician to make that decision.

Grounding AI with RAG

Rather than relying solely on a model’s training, RAG (retrieval-augmented generation) adds another layer of reliability by retrieving relevant information from trusted sources and using that information to guide the response.

This approach makes outputs more accurate and more defensible. If a patient is flagged as overdue for a screening, the system can point directly to the guideline supporting that conclusion.

When opposites attract, innovation begins

Reliable systems make it possible to move safely beyond documentation into clinical summaries, actionable insights and gap detection that can genuinely improve care. Deterministic guardrails and grounded sources provide the transparency clinicians need, making it clear why a value was flagged, which rule was applied or what guideline supports a recommendation.

The future of clinical AI is not a choice between intelligence and safety. It’s about building systems that combine the reasoning power of language models with authoritative evidence and deterministic safeguards. In healthcare, trust isn’t a compromise. It’s the foundation that makes innovation possible.