Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis
arXiv:2606.19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs. Structured machine-learning models offer more stable risk prediction, yet they require tabular inputs that are difficult to integrate with narrative clinical workflows. We present ClaMPAPP (Clinical Language-assisted Machine-learning Pipeline for Appendicitis), a hybrid system that uses an L
The development of hybrid LLM-ML systems for clinical decision support is emerging as researchers address the limitations of standalone LLMs and traditional ML models in real-world applications.
This development indicates a shift towards more robust and reliable AI applications in critical fields like medicine, balancing the interpretative power of LLMs with the stability of structured ML.
The approach to integrating AI into sensitive workflows is changing, moving from single-model dependency to hybrid architectures that leverage the strengths of different AI paradigms.
- · Healthcare providers
- · Patients
- · AI-in-health developers
- · Standalone LLM diagnostic engines
- · Purely tabular ML diagnostic systems
Improved diagnostic accuracy and accessibility in medical settings through hybrid AI systems.
Increased trust and adoption of AI tools in clinical practice due to enhanced reliability and interpretability.
The development of regulatory frameworks specifically for hybrid AI systems in medicine, allowing for broader deployment across various medical specialties.
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Read at arXiv cs.AI