Capabilities of Gemini Models in Medicine
Abstrak
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 complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain.
Penulis (67)
Khaled Saab
Tao Tu
Wei-Hung Weng
Ryutaro Tanno
David Stutz
Ellery Wulczyn
Fan Zhang
Tim Strother
Chunjong Park
Elahe Vedadi
Juanma Zambrano Chaves
Szu-Yeu Hu
Mike Schaekermann
Aishwarya Kamath
Yong Cheng
David G. T. Barrett
Cathy Cheung
Basil Mustafa
Anil Palepu
Daniel McDuff
Le Hou
Tomer Golany
Luyang Liu
Jean-baptiste Alayrac
Neil Houlsby
Nenad Tomasev
Jan Freyberg
Charles Lau
Jonas Kemp
Jeremy Lai
Shekoofeh Azizi
Kimberly Kanada
SiWai Man
Kavita Kulkarni
Ruoxi Sun
Siamak Shakeri
Luheng He
Ben Caine
Albert Webson
Natasha Latysheva
Melvin Johnson
Philip Mansfield
Jian Lu
Ehud Rivlin
Jesper Anderson
Bradley Green
Renee Wong
Jonathan Krause
Jonathon Shlens
Ewa Dominowska
S. M. Ali Eslami
Katherine Chou
Claire Cui
Oriol Vinyals
Koray Kavukcuoglu
James Manyika
Jeff Dean
Demis Hassabis
Yossi Matias
Dale Webster
Joelle Barral
Greg Corrado
Christopher Semturs
S. Sara Mahdavi
Juraj Gottweis
Alan Karthikesalingam
Vivek Natarajan
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓