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LLM framework MANANA improves epilepsy care in low-resource settings

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called MANANA to help large language models (LLMs) assist clinicians in underrepresented epilepsy care settings. This non-parametric prompt-learning framework adapts to local prescribing practices by learning from a small set of patient data. MANANA improves prescription accuracy and provides an uncertainty-based deferral signal, allowing the system to handle confident cases and defer less certain ones to specialists. AI

IMPACT This research demonstrates how LLMs can be adapted for specialized medical decision support in resource-constrained environments, potentially improving access to care.

RANK_REASON The cluster contains an academic paper detailing a new framework for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

LLM framework MANANA improves epilepsy care in low-resource settings

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The cluster contains an academic paper detailing a new framework for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shreyas Rajesh, Kartik Sharma, Tonmoy Monsoor, Mehmet Yigit Turali, Richard Idro, Juliana Kayaga, Robert Sebunya, Tracy Tushabe Namata, Jessica Nichole Pasqua, Vwani Roychowdhury, Rajarshi Mazumder ·

    Teaching LLMs to Recommend and Defer in Underrepresented Epilepsy Care

    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 a…

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