Search AI Products and News

Recently, MedBench, a well-known domestic medical large language model evaluation platform, released its latest rankings. Ant Group's medical team's self-developed medical large language model achieved outstanding performance, winning first place in both the evaluation and self-test rankings with high scores of 97.5 and 98.2 respectively, attracting widespread attention from the industry. The success of Ant Group's medical large language model is inseparable from the team's continuous efforts in researching and developing medical reasoning models. The team recently adopted reinforcement learning technology to create a new generation of medical reasoning models. This innovation has enabled the model to...

The research team from The Chinese University of Hong Kong (Shenzhen) and Shenzhen Big Data Institute has recently launched a medical large language model (LLM) named HuatuoGPT-o1. This model is specifically designed for complex reasoning in the medical field, aiming to enhance the reliability of medical diagnosis and decision-making. Unlike previous LLMs that focused on mathematical reasoning, HuatuoGPT-o1 specializes in the medical domain by simulating the rigorous thought processes of doctors in real-world practice, paving new avenues for the development of medical AI.

Google recently launched the Health AI Developer Foundation (HAI-DEF), a health AI developer initiative designed to empower developers to efficiently build and implement medical AI models. The goal of this new initiative is to democratize AI development in the healthcare sector, promote innovation, and improve patient care. Unique challenges faced in medical AI development include the demand for large, diverse datasets, the need for expertise in both AI and healthcare, and the complexities of training and deploying these models.

Recently, NVIDIA and Microsoft launched a new initiative aimed at promoting AI innovation in the healthcare sector. This collaboration combines NVIDIA's Inception global program with Microsoft's startup support program, intending to assist more startups in enhancing their medical devices and workflows. Currently, the two companies have hundreds of startups participating in these ecosystems. Note: The image is AI-generated and licensed through Midjourney. Through this collaboration, startups will have easier access to acceleration resources.

Recently, pioneering company in the field of clinical AI, Aidoc, announced that it is collaborating with NVIDIA to develop a new guide called 'BRIDGE' (Blueprint for Resilient Integration and Deployment of Guided Excellence). The goal of this guide is to accelerate the adoption of artificial intelligence in the healthcare industry, with an expected release in early 2025.

Baichuan Intelligence, a large model startup, recently completed its Series A financing round, raising a total of 5 billion yuan and achieving a valuation of 20 billion yuan. According to an exclusive report by 'Intelligent Emergence', in addition to previously announced investors such as Alibaba, Tencent, and Xiaomi, this financing round has also attracted industrial investment funds with state-owned backgrounds from Beijing, Shanghai, and Shenzhen.

On July 5th, at the 2024 World Artificial Intelligence Conference, Alipay launched one of the first domestic multimodal medical large models. This model, based on the multimodal capabilities of Anting, can directly understand and train multimodal data such as audio, video, images, and text. During the testing phase, the accuracy of recognition for medical reports, pharmaceuticals, and hair conditions reached over 90%, and the Chinese and English medical exams and benchmark tests reached or excee

Shanghai AI Lab, together with Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, has released the medical multimodal foundational model group 'PuYi 2.0'. PuYi 2.0 adds multi-domain models and incremental language parameters, covering various data modalities such as medical images, medical texts, and bioinformatics. New open-source datasets include the medical image segmentation dataset SA-Med2D-20M and the pathology dataset SNOW. PuYi 2.0 incorporates an evaluation module to provide reference implementations for medical model capabilities, delivering a one-stop open-source solution for large medical models.

["The WHO has released a report calling for strengthened regulation of medical AI.","The report emphasizes the establishment of safe and effective medical AI systems to promptly provide appropriate systems to those in need.","The report highlights the importance of transparency, risk management, and data validation.","The report recommends enhancing regulation to manage risks arising from biases in AI training data.","The report calls for ensuring the quality of AI training data to avoid amplifying biases."]