arXiv:2601.22324v2 Announce Type: replace Abstract: Modern clinical practice relies on evidence-based guidelines implemented as compact scoring systems composed of a small number of interpretable decision rules. While machine-learning models achieve strong performance, many fail to translate into routine clinical use due to misalignment with workflow constraints such as memorability, auditability, and bedside execution. We argue that this gap arises not from insufficient predictive power, but from optimizing over model classes that are incompatible with guideline deployment. Deployable guideli
The proliferation of advanced LLMs enables agentic systems to tackle complex domain-specific tasks, leading to their application in specialized fields like clinical medicine.
This development addresses a critical gap in clinical AI, moving beyond predictive power to create deployable, interpretable, and auditable systems essential for real-world medical adoption.
The focus shifts from general machine learning models to bespoke LLM agent systems capable of constructing clinical decision tools that align with practical clinical workflow constraints.
- · Healthcare providers
- · Patients
- · AI developers specializing in healthcare
- · Clinical decision support systems
- · Traditional ML model developers (without agentic focus)
- · Non-interpretable AI solutions in healthcare
Clinical scoring systems become more widely adopted and trusted due to improved interpretability and auditability.
The development and deployment cycle for new clinical guidelines could accelerate significantly, directly impacting patient care.
This success may pave the way for LLM agents to construct explainable decision systems in other highly regulated and sensitive industries.
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Read at arXiv cs.LG