arXiv:2606.31036v1 Announce Type: new Abstract: Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managing longitudinal treatment. Such systems must adapt to local prescribing practice and know when to defer. We study this problem in Ugandan pediatric epilepsy care, predicting anti-seizure medication regimens from longitudinal unstructured clinic notes. Standard prompting achieves non-trivial agreement with physician prescriptions, but neurologist review shows that many errors reflect distribution-mis
The proliferation of advanced LLMs and increasing demand for healthcare access in resource-constrained regions are creating significant opportunities for AI-driven solutions.
This development demonstrates a crucial step towards deploying LLMs in sensitive, real-world clinical settings, addressing not just recommendation but also crucial aspects of localized adaptation and safe deferral.
The focus moves from merely achieving diagnostic or treatment agreement to developing LLMs that can ethically integrate into existing healthcare systems, recognizing their limitations and varying local practices, especially in underserved areas.
- · AI in healthcare developers
- · Patients in resource-constrained regions
- · Frontline clinicians
- · Ugandan healthcare system
- · Traditional medical software companies slow to adapt AI
- · Centralized specialist care models
Increased adoption and trust in AI-powered decision support systems within global healthcare.
Development of regulatory frameworks and best practices for ethical AI deployment in diverse medical contexts, particularly concerning adaptability and deference.
Re-evaluation of medical training curricula to include AI interaction and oversight, potentially leading to new healthcare delivery paradigms globally.
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