Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules
arXiv:2606.16337v1 Announce Type: new Abstract: Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic. Although deep learning and tree-based ensemble methods can achieve high accuracy, their black-box nature remains a major obstacle to clinical deployment. This challenge is further compounded by common characteristics of medical data, including limited sample sizes, severe class imbalance, and feature evolution arising from changes in diagnostic criteria and clinica
The increasing maturity of LLMs provides the capability to address long-standing challenges in explainable AI for critical domains like medicine, making this research timely.
This development moves AI closer to practical, auditable clinical deployment by tackling the black-box problem in medical decision support, which is a major regulatory and ethical hurdle.
Clinical decision support systems can now potentially leverage advanced AI with transparent decision logic, fostering greater trust and accelerating adoption in healthcare settings.
- · Healthcare providers
- · Patients
- · AI/ML developers in healthcare
- · Medical technology sector
- · Developers of opaque black-box medical AI systems
- · Traditional clinical decision support systems
Improved patient outcomes due to more reliable and interpretable AI-driven medical decisions.
Accelerated regulatory approval for AI tools in medicine as transparency concerns are mitigated.
A potential shift in medical training to incorporate AI-assisted diagnostics and treatment planning as standard practice.
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Read at arXiv cs.AI