Does Higher Accuracy Make Veterinary AI Safer?
A Review of Safety and Accuracy Follow Different Scaling Laws in Clinical Large Language Models
The use of large language models (LLMs) in medical AI and clinical decision support systems (CDSS) is expanding rapidly.
We often assume that larger models, longer context windows, more sophisticated retrieval, and greater inference-time compute will not only improve accuracy, but also make AI systems safer.
But does higher average accuracy necessarily mean a model is clinically safer?
A recent preprint, Safety and Accuracy Follow Different Scaling Laws in Clinical Large Language Models, addresses this question directly.
In clinical LLMs, accuracy and safety may improve in the same direction, but they are not the same metric.
This distinction matters especially in medical and veterinary AI, where model outputs may influence real-world clinical decisions. In such environments, average accuracy alone may not be enough to evaluate whether a system is safe to deploy.
SaFE-Scale and RadSaFE-200
The study introduces the SaFE-Scale framework and the RadSaFE-200 benchmark for evaluating the safety of clinical LLMs.
RadSaFE-200 consists of 200 radiology multiple-choice questions. Rather than simply labeling each response as correct or incorrect, the benchmark assigns clinician-defined safety labels to individual answer options.
These include:
- High-Risk Error — an incorrect answer that could potentially cause clinical harm
- Unsafe Answer — an answer that directly conflicts with established guidelines
- Contradiction — an answer that contradicts the evidence provided
- Dangerous Overconfidence — a high-risk incorrect answer stated with ≥80% confidence
This approach shifts evaluation from a simple question -
“Did the model get the answer right?”
- to a more clinically relevant one -
“How dangerous is the model when it gets the answer wrong?”
Key Finding: Accuracy and Safety Can Decouple
The researchers evaluated 34 LLMs, including models from the Qwen, Llama, Gemma, MedGemma, DeepSeek, Mistral, and OpenAI-OSS families, across six deployment settings.
The findings offer several important implications for developers of medical AI and CDSS.
The most striking result was the impact of clean evidence—high-quality evidence written or curated by clinicians.
As a single intervention, clean evidence produced the largest improvement in safety, and all 34 models moved in the same direction.
Average Accuracy
73.5% → 94.1%
High-Risk Errors
12.0% → 2.6%
Dangerous Overconfidence
8.0% → 1.6%
These results suggest that improving the quality of evidence may have a more direct impact on clinical AI safety than simply increasing model size.
By contrast, standard RAG and agentic RAG produced only modest gains in average accuracy, from 76.0% to 78.1%, while failing to sufficiently reduce high-risk errors and dangerous overconfidence.
Notably, agentic RAG improved accuracy over standard RAG, but dangerous overconfidence actually increased from 5.7% to 8.0%.
The implication is important:
Retrieval that improves accuracy is not necessarily retrieval that improves safety. Retrieval-based clinical LLMs therefore need to be evaluated not only for answer accuracy, but also for the high-risk errors that remain after retrieval.
Max-context prompting increased latency while providing limited safety gains, and self-consistency produced only marginal improvements in both accuracy and safety.
A three-model ensemble also improved average accuracy, but introduced a new failure mode: synchronized failure, in which multiple models converged on the same incorrect answer.
In other words:
Agreement between multiple models does not guarantee clinical safety.
Can Confidence Serve as a Safety Filter?
Many AI systems use confidence scores as a proxy for reliability.
However, the study suggests that confidence alone may be insufficient as a safety filter for clinical LLMs.
Under closed-book conditions, the average confidence for high-risk incorrect answers was 87.8%, compared with 94.9% for correct answers—a relatively small difference.
Even under the clean-evidence condition, confidence in the remaining incorrect answers stayed high at 85.4%.
This leads to an important conclusion:
The safety benefit of clean evidence came from reducing the number of errors—not from making incorrect answers less confident.
A deployment strategy that simply rejects answers below a confidence threshold may therefore be insufficient.
For medical and veterinary AI, more structural safety mechanisms may be required.
What This Means for Medical and Veterinary AI
The study was conducted in the relatively constrained setting of radiology multiple-choice questions, so its findings should not be generalized without caution.
Still, it points toward two important changes in how clinical LLMs may need to be evaluated.
First, benchmarks should include option-level safety labels, rather than relying on accuracy alone.
Second, retrieval pipelines should be evaluated based on the high-risk errors that remain after retrieval.
If improvements in average accuracy do not necessarily translate into equivalent improvements in clinical safety, then accuracy and safety need to be measured and monitored separately during deployment.
Three findings are particularly relevant for clinical LLM system design:
Evidence Quality Can Matter More Than Model Scale Confidence Is Not a Reliable Safety SignalModel Agreement Does Not Guarantee Safety
CHOONOK Company’s View: Veterinary AI Is Not About Generating the “Right Answer”
For veterinary clinical AI, this study suggests four important principles.
First, majority voting across similar models should not be treated as a safety mechanism. Models trained on similar data and reasoning patterns may fail in similar ways—and may confidently converge on the same wrong answer.
Second, high-quality evidence may matter more than model size. Large volumes of clinical data do not automatically become an advantage. The real value may come from transforming raw data into carefully curated, clinically meaningful evidence.
Third, veterinary AI benchmarks should evaluate more than accuracy. They should also measure how often the system produces high-risk errors and how confidently it presents incorrect answers.
Fourth, systems should know when to defer to the veterinarian. When evidence is insufficient or clinical risk is high, the system should be designed to support—not replace—professional judgment.
In clinical practice, one of the most dangerous failure modes is not simply being wrong.
It is being wrong with confidence.
Accuracy and safety should therefore be treated as related, but distinct, performance dimensions.
At CHOONOK Company, we view veterinary AI not as a technology for simply generating answers, but as a system designed to support safer, more explainable clinical decision-making.
The future competitiveness of veterinary AI will not come from using the largest model alone.
What matters more is how we design decision-support systems in which veterinarians and AI work together around evidence they can trust, reduce high-risk errors, and manage both accuracy and safety as separate but equally important objectives.
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