Prospective evaluation of a large language model clinical decision support system in the emergency department.

Liron Leibovitch¹, Adi Ahituv^1,2, Alon Gorenshtein³, Dvir Aran^4,5, Moran Sorka^2,4, Keren Miron¹, Shahar Shelly^6,7,8

¹Department of Neurology, Rambam Health Care Campus, Haifa, Israel.

²Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel.

³Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.

⁴Faculty of Biology, Technion - Israel Institute of Technology, Haifa, Israel.

⁵Taub Faculty of Computer Science Technion - Israel Institute of Technology, Haifa, Israel.

⁶Department of Neurology, Rambam Health Care Campus, Haifa, Israel. shahar.shell@technion.ac.il.

⁷Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel. shahar.shell@technion.ac.il.

⁸Department of Neurology, Mayo Clinic, Rochester, MN, USA. shahar.shell@technion.ac.il.

PubMed DOI

Abstract

Prospective evidence for artificial intelligence (AI)-based clinical decision support in emergency departments remains limited. Here we conducted a DECIDE-AI stage 1 evaluation of SHAKED, a clinical decision support system built on multiple large language models, in a tertiary emergency department. Over 4 weeks, 1,138 patients were analyzed across two parallel units-one using SHAKED and one follow…

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