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Microsoft CEO Satya Nadella recently announced on a social platform that Microsoft has officially launched the revolutionary medical AI system MAI-DxO. This innovative system stands out with its unique "model-agnostic" design, allowing it to flexibly adapt to language models of different manufacturers and capabilities, thereby significantly improving their diagnostic performance. More excitingly, MAI-DxO is not only able to simulate the diagnostic process of real doctors, but also demonstrated diagnostic accuracy far exceeding that of professional physicians in tests, while greatly reducing the cost of medical diagnosis. Microsoft has released test data.

Ces lunettes intelligentes à véritable IA adopteront une architecture à double puce et intégreront un appareil photo dont les performances devraient rivaliser avec celles des Meta Ray-Ban. Elles pourront non seulement percevoir le monde réel grâce à leur objectif, mais aussi proposer une expérience interactive riche et variée grâce à l’IA. Par exemple, les utilisateurs pourront identifier des objets, reconnaître automatiquement les restaurants et obtenir instantanément des informations pratiques telles que les prix et les notes, une fonctionnalité similaire au système de vision intelligent d’Apple.

The University of Science and Technology of China (USTC) recently announced a significant breakthrough. Professor Sun Cheng's research group and collaborators have successfully developed the "TIMES" scoring system for predicting liver cancer recurrence, and have translated it into a publicly available, free AI diagnostic tool. The findings were published in the international academic journal Nature on March 13th, Beijing time. Liver cancer, the third leading cause of cancer-related deaths globally, has a recurrence rate of up to 70% after surgical resection. Accurate prediction of liver cancer recurrence has long been a medical challenge. The spatial distribution of immune cells in the tumor microenvironment significantly impacts patient prognosis, but previously...

In recent years, the application of artificial intelligence in the medical field has been expanding. A recent study indicates that the chatbot ChatGPT-4, developed by OpenAI, has surpassed doctors in diagnostic accuracy, sparking widespread discussion and attention. The study, published in the Journal of the American Medical Association Network Open, tested the performance of 50 doctors on six complex cases. Results showed that doctors using ChatGPT scored an average of 76%, while those not using the tool scored only 74%. Shockingly, Chat

Artificial intelligence is quietly transforming the diagnostic methods in the field of mental health. A research team from Kaunas University of Technology has developed a revolutionary depression diagnosis model that employs multimodal analysis of voice and EEG data, paving the way for precise identification of mental health issues. The core of this research lies in breaking the limitations of traditional single-data diagnostics. The research team chose voice as a key data source because it can subtly reflect emotional states. Speech rate, tone, and emotional energy can all serve as potential signals of depression.

During the themed event on 'The Deep Empowerment of AI Technology in Primary and Secondary Education', Li Ying, CEO of Baidu Xiao Du Technology, shared the latest developments in Xiao Du's education business. As of July 2024, the Xiao Du Learning Machine has surpassed 2 million cumulative users, with more than 50% being daily active users, indicating its popularity. The usage rate of AI teachers has increased by 3.5 times compared to traditional methods, with users spending an additional 25% time on the learning machine, totaling 114 minutes per day. Notably, during the immersive interactive preview section for Chinese language, the average usage time reached 22 minutes.

Scientists at University College London's Institute of Neurology have developed a new tool based on AI language models that can characterize subtle features in the speech of schizophrenia patients. The research aims to understand how automated language analysis can improve the diagnosis and assessment of mental disorders. The study found that the language performance of schizophrenia patients was less predictable by AI models compared to the control group, which may relate to brain learning and cognitive mapping. Scientists plan to widely apply this technology, potentially leading to breakthroughs in the diagnosis of mental illness in the future.