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.

Gemini 模型在医学应用领域的卓越表现面临着挑战，要求先进的推理能力、获取最新的医学知识和理解复杂的多模态数据。该研究引入了 Med-Gemini，一类在医学领域具有高度实用性的多模态模型，能够无缝地使用网络搜索，并可以通过定制编码器高效地适应新的模态特征。在 14 个医学基准测试中，Med-Gemini 取得了新的最佳性能，超过了 GPT-4 模型系列，并在多模态基准测试中取得了相对较大的提升。该研究通过突破先前的方法，在长期匿名健康记录和医学视频问答方面展示了 Med-Gemini 的长语境能力和非凡性能，并在医学文本摘要等任务上超越人类专家。尽管在这个安全关键领域中，需要进一步的严格评估，但我们的结果提供了 Med-Gemini 潜力的有力证据。