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Yiftach Barash on Muck Rack

Yiftach Barash

As seen in: Nature, MDPI, Postgraduate Medical Journal, medRxiv
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Articles

Reference quality of OpenEvidence across five medical specialties

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By Tamir Lotan, Mahmud Omar, Yiftach Barash, Jonathan B. Kruskal, Muneeb Ahmed, Olga R. Brook, Eyal Klang, Alon Gorenshtein
| Nature Verified
Abstract OpenEvidence (OE) is a retrieval-augmented clinical AI tool. We evaluated the references OE returned to 150 standardized prompts across five specialties, analyzing all 4979 citations. No reference was confirmed as fabricated; three abstracts had attribution errors. Most references were recent and high-impact, though journal concentration varied by specialty.
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Assigned roles change how clinical AI agents allocate shared resources

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By Alon Gorenshtein, Mahmud Omar, Yiftach Barash
| medRxiv
The authors have declared no competing interest. I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
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How to meaningfully evaluate AI in clinical medicine

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By Mahmud Omar, Reem Agbareia, Alon Gorenshtein, Ashwin Ramaswamy, Ankit Sakhuja, Yiftach Barash, Eyal Klang, Girish N. Nadkarni, Daniel S. W. Ting
| Nature Verified
The people who follow the emerging evidence on large language models (LLMs) in clinical medicine will notice something unusual. A systematic review of 30 studies found diagnostic accuracy ranging from 25% to 98%, a nearly fourfold spread across similar models on similar tasks1. Minor changes in prompt wording and format can shift model performance substantially, even when the underlying clinical task is unchanged2.
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