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."]