What does this research mean for the field?

A calibrated Monte-Carlo risk gate using semantic representations significantly outperforms traditional keyword and regular-expression filters in safely gating multilingual colorectal-cancer LLM dialogues, successfully blocking 98.2% of harmful prompts. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and evaluate a calibrated selective-prediction risk gate that detects unsafe user queries in multilingual colorectal-cancer large language model interactions and appropriately routes them to clinical care.

September 15, 2026Big Data and Cognitive ComputingOpen Access

Beyond Keyword Filters: Calibrated Monte-Carlo Risk Gating for Safe Multilingual Colorectal-Cancer LLM Dialogue

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Authors

AKAbdurrahim Kızılay KGKerem Gencer

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Overview

Validation study demonstrates calibrated risk gating blocks 98.2% of harmful queries in multilingual colorectal cancer dialogue, highlighting a reliable safeguard against unsafe medical AI advice.

Key Points

To develop and evaluate a calibrated selective-prediction risk gate that detects unsafe user queries in multilingual colorectal-cancer large language model interactions and appropriately routes them to clinical care.
Designed a selective-prediction framework that estimates query risk using a bootstrap ensemble over multilingual sentence representations calibrated via Platt scaling.
Evaluated the risk gate against keyword, regular-expression, and fuzzy filters on a scenario-level split of 450 oncologist-approved prompts across English, Turkish, and Spanish.
The risk gate achieved a guardrail F1 of 0.961, blocking 98.2% of harmful prompts and refusing 10.8% of legitimate questions, significantly outperforming keyword and regex baselines that peaked at an F1 of 0.034 (corrected q = 0.0003).
Platt scaling reduced the expected calibration error from 0.216 to 0.069, and the ensemble predicted its own errors with an AUROC of 0.856, eliminating errors entirely at 50% coverage.

Cite This Study

Kızılay et al. (2026) studied this question.

synapsesocial.com/papers/6aa913a29013453be30a1bf1 https://doi.org/10.3390/bdcc10090317

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September 15, 2026Big Data and Cognitive ComputingOpen Access

Beyond Keyword Filters: Calibrated Monte-Carlo Risk Gating for Safe Multilingual Colorectal-Cancer LLM Dialogue

View Full Paper

Ask AI

Bookmark

Authors

AKAbdurrahim Kızılay KGKerem Gencer

Discussion

Member takes

Overview

Validation study demonstrates calibrated risk gating blocks 98.2% of harmful queries in multilingual colorectal cancer dialogue, highlighting a reliable safeguard against unsafe medical AI advice.

Key Points

To develop and evaluate a calibrated selective-prediction risk gate that detects unsafe user queries in multilingual colorectal-cancer large language model interactions and appropriately routes them to clinical care.
Designed a selective-prediction framework that estimates query risk using a bootstrap ensemble over multilingual sentence representations calibrated via Platt scaling.
Evaluated the risk gate against keyword, regular-expression, and fuzzy filters on a scenario-level split of 450 oncologist-approved prompts across English, Turkish, and Spanish.
The risk gate achieved a guardrail F1 of 0.961, blocking 98.2% of harmful prompts and refusing 10.8% of legitimate questions, significantly outperforming keyword and regex baselines that peaked at an F1 of 0.034 (corrected q = 0.0003).
Platt scaling reduced the expected calibration error from 0.216 to 0.069, and the ensemble predicted its own errors with an AUROC of 0.856, eliminating errors entirely at 50% coverage.

Cite This Study

Kızılay et al. (2026) studied this question.

synapsesocial.com/papers/6aa913a29013453be30a1bf1 https://doi.org/10.3390/bdcc10090317

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