There have been great improvements in clinical reasoning, multimodal understanding and long text processing.
Researchers used 14 medical benchmarks to test Med-Gemini’s capabilities.
It was found that it achieved the best performance on all 10 benchmarks, far exceeding the previous strongest GPT-4 model.
For example, on the popular medical question-and-answer test MedQA,Med-Gemini achieved an accuracy rate of 91.1%, which is 4.6% higher than the previous best model.
Med-Gemini is not only good at text tasks, but also good at understanding multimodal data such as medical images, videos, and electrocardiograms. It can read medical images and answer relevant questions. You can also watch medical teaching videos and master the surgical procedures.
In addition,Med-Gemini can quickly read lengthy medical records, identify key information, and summarize the patient’s main medical conditions. In some real-life medical tasks, such as medical record summaries and referral letter writing, its performance even exceeds that of human doctors.
The excellent performance of various medical applications poses huge challenges to artificial intelligence, requiring advanced reasoning, acquisition of the latest medical knowledge, and understanding of complex multimodal data. The Gemini model has strong universal capabilities in multi-modal and long-context reasoning, offering exciting possibilities for the medical field.
Building on these core strengths of Gemini, we have launched Med-Gemini, a powerful family of multimodal models specifically used in medicine, capable of seamless use of web search, and can be used to effectively target novel patterns using custom encoders.
Med-Gemini was evaluated on 14 medical benchmarks, established new state-of-the-art (SoTA) performance on 10 of them, and surpassed the GPT-4 model family on each benchmark that could be directly compared, often for broad profits.
On the popular MedQA (USMLE) benchmark, the Med-Gemini model uses a novel uncertainty guided search strategy to achieve SoTA performance with a 91.1% accuracy. In seven multimodal benchmarks, including NEJM Image Challenges and MMMU (Health and Medicine), Med-Gemini improved its average comparative advantage over GPT-4V by 44.5%.
SoTA performance for needle in a haystack retrieval tasks from long-term de-identified health records and medical video Q & A demonstrates the effectiveness of Med-Gemini’s long-context capabilities, surpassing previous customized methods that used only context learning. Finally, Med-Gemini’s performance demonstrates its practicality in the real world, surpassing human experts in tasks such as medical text summaries, while demonstrating the huge potential of multimodal medical dialogue, medical research and education.
Overall, our results provide convincing evidence of the potential of Med-Gemini, although further rigorous assessment is crucial before actual deployment in this security-critical area.
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Paper address:https://arxiv.org/abs/2404.18416
Video: