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Decision model adapted for clinical intelligence achieves 95% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel approach for clinical intelligence by adapting a 9-billion-parameter decision model, Clef-Flash, built on a Qwen 3.5 backbone. This model achieved 95.42% diagnostic accuracy on the Symptom2Disease benchmark using parameter-efficient fine-tuning with QLoRA. The system demonstrates a significant speedup, with inference times under 80 milliseconds per record, representing a 31-fold improvement over generative LLM baselines. Additionally, a new counterfactual perturbation framework, Leave-One-Symptom-Out, was introduced to assess symptom-level evidence and interpretability. AI

IMPACT Demonstrates potential for low-latency, interpretable AI in clinical settings, improving diagnostic speed and accuracy.

RANK_REASON Research paper detailing a new model adaptation and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Decision model adapted for clinical intelligence achieves 95% accuracy

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19 / 100
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Research paper detailing a new model adaptation and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · S. M. Mahbub Uddin Ahmed ·

    Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models

    arXiv:2610.15029v1 Announce Type: cross Abstract: Automated disease classification from patient-reported symptoms requires balancing the speed of lightweight discriminative models against the contextual understanding and latency costs of autoregressive large language models (LLMs…

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