Understanding AI Hallucinations in Healthcare: Risks and Safeguards

As AI becomes increasingly integrated into healthcare workflows, one concern consistently emerges: What happens when AI systems generate inaccurate or fabricated information? This phenomenon -often called “AI hallucinations” -presents unique challenges in clinical settings where accuracy is paramount.

What Are AI Hallucinations?

AI hallucinations occur when large language models (LLMs) generate plausible-sounding but incorrect or fabricated information. This can manifest as:

• Citing non-existent research studies
• Providing inaccurate clinical recommendations
• Generating confident-sounding but wrong diagnoses
• Creating fictional patient history details

Some researchers prefer the term “AI misinformation” to avoid stigmatizing associations with human hallucinations -a valid consideration in mental health contexts.

Why Healthcare Is Different

The stakes in healthcare are uniquely high. Unlike an AI chatbot giving incorrect restaurant recommendations, AI misinformation in clinical settings can directly impact patient safety, treatment decisions, and health outcomes.

Healthcare professionals must understand that LLMs are only as accurate as their training data, and even well-trained models can generate confident-sounding falsehoods -particularly when asked about edge cases, recent developments, or highly specific clinical scenarios.

How Videra Health Addresses Reliability

At Videra Health, we’ve built our AI systems with reliability as a foundational principle. Our approach differs fundamentally from general-purpose LLMs:

Validated Clinical Models

Our AI is trained specifically for behavioral health assessment, using clinically validated datasets and standardized measures. Rather than generating free-form text, our systems analyze specific patterns -facial expressions, speech characteristics, movement patterns -against validated clinical criteria.

Human-in-the-Loop Design

We position AI as clinical decision support, never replacement. Our systems flag concerns and surface insights, but clinical decisions always remain with qualified healthcare professionals. Every alert, every recommendation can be reviewed, validated, and acted upon by human clinicians.

Transparent Confidence Scoring

When our AI provides assessments, it includes confidence metrics. Clinicians can see not just what the system detected, but how confident it is in that detection -enabling appropriate clinical judgment about how to weight AI input.

Continuous Validation

We regularly validate our algorithms against human clinical raters, publishing research on agreement metrics like Cohen’s Kappa. Our TDScreen tool, for example, demonstrated a Kappa of 0.61 -actually exceeding agreement between human raters.

Best Practices for Clinicians

Healthcare professionals integrating AI tools should:

1. Understand the tool’s scope - Know what the AI is designed to do, and don’t use it outside that scope
2. Verify unexpected findings - AI insights should inform, not replace, clinical judgment
3. Maintain documentation standards - AI-generated notes should be reviewed before signing
4. Report anomalies - Help improve systems by reporting when AI outputs seem wrong
5. Stay educated - AI capabilities are evolving rapidly; ongoing learning is essential

The Path Forward

The solution to AI hallucinations isn’t to avoid AI in healthcare -it’s to implement AI thoughtfully, with appropriate safeguards and human oversight. When built on validated data, deployed within clear boundaries, and integrated with clinical workflows that maintain human decision-making authority, AI can enhance rather than undermine the quality of patient care.

At Videra Health, that’s exactly the approach we take. Our AI is a tool that makes clinicians more effective -never a replacement for clinical expertise.