activity
20232026
most citedCapabilities of Gemini Models in Medicine

96 citations · 263 across the 12 of their papers we have counts for

collaborators

13 papers

cs.AI2026

Accelerating Scientific Research with Gemini in the Real-World

Samuel Schmidgall, Xiaokai Zhu, Marian Shaw +32

We present an extension and comprehensive real-world validation of Co-Scientist, a Gemini-based multi-agent system designed to accelerate end-to-end scientific research across hypo…

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.AI2025★ 87 cited

Accelerating scientific discovery with Co-Scientist

Juraj Gottweis, Wei-Hung Weng, Alexander Daryin +48

Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce C…

cs.HC2024

Exploring Large Language Models for Specialist-level Oncology Care

Anil Palepu, Vikram Dhillon, Polly Niravath +18

Large language models (LLMs) have shown remarkable progress in encoding clinical knowledge and responding to complex medical queries with appropriate clinical reasoning. However, t…

cs.HC2024★ 3 cited

Towards Democratization of Subspeciality Medical Expertise

Jack W. O'Sullivan, Anil Palepu, Khaled Saab +23

The scarcity of subspecialist medical expertise, particularly in rare, complex and life-threatening diseases, poses a significant challenge for healthcare delivery. This issue is p…

cs.CV2024★ 33 cited

Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset

Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen +37

The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previou…

cs.CV2024★ 30 cited

Advancing Multimodal Medical Capabilities of Gemini

Lin Yang, Shawn Xu, Andrew Sellergren +44

Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Buil…

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…

cs.LG2024★ 9 cited

HeAR -- Health Acoustic Representations

Sebastien Baur, Zaid Nabulsi, Wei-Hung Weng +15

Health acoustic sounds such as coughs and breaths are known to contain useful health signals with significant potential for monitoring health and disease, yet are underexplored in…

cs.LG2023

Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals

Louis Blankemeier, Sebastien Baur, Wei-Hung Weng +5

Health-related acoustic signals, such as cough and breathing sounds, are relevant for medical diagnosis and continuous health monitoring. Most existing machine learning approaches…