activity
20242026
most citedCapabilities of Gemini Models in Medicine

96 citations · 101 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2026

CARE: A Conformal Safety Layer for Medical Summarization

Suhana Bedi, Bridget Lin, Anson Y. Zhou +5

Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims. Existing…

cs.AI2026

Towards Conversational Medical AI with Eyes, Ears and a Voice

Meet Shah, Jason Gusdorf, Anil Palepu +50

The practice of medicine relies not only upon skillful dialogue but also on the nuanced exchange and interpretation of rich auditory and visual cues between doctors and patients. B…

cs.HC2025

Complementary Human-AI Clinical Reasoning in Ophthalmology

Mertcan Sevgi, Fares Antaki, Abdullah Zafar Khan +26

Vision impairment and blindness are a major global health challenge where gaps in the ophthalmology workforce limit access to specialist care. We evaluate AMIE, a medically fine-tu…

cs.CL2025

Advancing Conversational Diagnostic AI with Multimodal Reasoning

Khaled Saab, Jan Freyberg, Chunjong Park +33

Large Language Models (LLMs) have demonstrated great potential for conducting diagnostic conversations but evaluation has been largely limited to language-only interactions, deviat…

cs.CL2025★ 3 cited

Towards Conversational AI for Disease Management

Anil Palepu, Valentin Liévin, Wei-Hung Weng +17

While large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning - including disease progression, therapeutic res…

cs.AI2024★ 96 cited

Capabilities of Gemini Models in Medicine

Khaled Saab, Tao Tu, Wei-Hung Weng +64

Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of…

cs.LG2024★ 2 cited

Mitigating LLM Hallucinations via Conformal Abstention

Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz +9

We develop a principled procedure for determining when a large language model (LLM) should abstain from responding (e.g., by saying "I don't know") in a general domain, instead of…