Currently, large-scale basic models, multimodal datasets, and large-scale research on biomolecules such as DNA and proteins are refreshing people's cognition at an unimaginable speed. The World Economic Forum website recently reported that Generative Artificial Intelligence (GenAI) has become one of the strongest engines driving progress in life sciences. It further unleashes the potential of biotechnology such as CRISPR gene editing and cell engineering. Nowadays, GenAI has emerged in fields such as drug development, precision medicine, and brain computer interfaces, demonstrating remarkable application prospects. In the next 10 years, it will write a new chapter for human health. In January of this year, McKinsey&Company released an analysis report stating that its global research institute had predicted that GenAI could create approximately $60 billion to $110 billion in economic value for the pharmaceutical and healthcare industries annually. This technology will significantly improve the efficiency and innovation level of the entire industry chain, from the way new drugs are developed to the promotion and management mode of medical services, all of which will undergo profound changes. In drug development, GenAI can identify new targets and optimize molecular structure design. In the development phase, it simplifies the preclinical validation process, designs safer and more efficient delivery systems for cell and gene therapies, and helps build smarter clinical trial protocols. McKinsey's analysis further points out that GenAI can help pharmaceutical companies cope with the challenge of "asset lifecycle compression" by accelerating the development, approval, and market launch of therapies, which means that the time window for companies to obtain value from new drugs is constantly narrowing. Research has found that over the past 20 years, this cycle has shortened from 9.8 years to approximately 7.1 years, a total reduction of 18 months. In addition, GenAI can simulate patient populations, predict treatment outcomes, and combine real data from electronic health records and wearable devices to shorten the transition path from concept to clinical practice. Assisting precision medicine to enter a new stage. Precision medicine is an innovative medical concept that comprehensively considers factors such as each person's genetic characteristics, living environment, and lifestyle when formulating treatment plans, truly achieving a "personalized" approach. This shift from a one size fits all approach to personalized care is advancing at an unprecedented pace with the empowerment of GenAI. In 2024, most GenAI applications in the healthcare field will still primarily rely on text-based unimodal large language models. By 2025, multimodal models capable of simultaneously processing and generating text, images, genomic data, and even real-time monitoring of vital signs are gradually becoming mainstream. This progress will significantly improve diagnostic efficiency, clinical decision support capabilities, and medical imaging analysis levels, injecting strong momentum into precision medicine. These GenAI applications can accurately analyze multimodal data including genetic information, lifestyle, and environmental factors, providing highly personalized diagnosis, monitoring, and treatment recommendations for patients. According to a report from the US Health Industry Trends website, GenAI can interpret patients' genetic profiles, recommend the most effective drugs, and minimize trial and error medication. It can also provide targeted suggestions for improving lifestyle based on genetic predisposition, helping people actively manage their health. Combining real-time health data collected by wearable devices, GenAI can even dynamically adjust treatment plans to achieve dynamic optimization of treatment. Opening up a new path of human-computer interaction, brain computer interface is a technology that allows people to directly communicate with digital devices and even the external world through thinking. GenAI is gradually achieving seamless decoding of neural signals, converting brain activity into control commands for external devices. When GenAI and brain computer interfaces are strongly combined, a two-way communication bridge is built between biological cognition and computing systems, which not only brings new tools for assisted living to patients, but also opens up a new path for human-computer interaction to expand human potential. In early September this year, a research team from the University of California, Los Angeles published a result in Nature Machine Intelligence. They have developed a non-invasive brain computer interface system and equipped it with two "AI assistants": one can assist users in manipulating the computer cursor, and the other can use virtual input to assist in completing robotic arm tasks. Tests have shown that the system significantly improves the performance of paralyzed patients in tasks such as operating the cursor or robotic arm by interpreting user intent and assisting in executing actions. The third participant of the "Neural Connection" company successfully controlled the robotic arm through brainwaves to complete the complex action of "grasping delivery". At the same time, the AI model LaBraM synchronously reverse analyzed the neural encoding corresponding to the action. This marks a new stage in the construction of bidirectional neural symbol systems, where the interaction between the human brain and AI is no longer limited to one-way instruction transmission. This technological transformation has the potential to quietly reshape the fundamental paradigms of healthcare, education, and even human cognition. McKinsey&Company emphasizes that in the field of medicine and health, GenAI is not just a tool, but an important force driving comprehensive innovation in patient care, medical research, and health management systems. However, the development of GenAI still faces several challenges. The first and foremost issue is the interpretability and transparency of the model, especially the GenAI model based on deep learning algorithms, which is often criticized for its "black box" characteristics, making it difficult for medical staff to understand its decision-making logic. This lack of interpretability has to some extent affected trust and hindered the widespread application of technology. In addition, training AI models requires large-scale datasets, which has also raised deep concerns about data privacy and security issues. (New Society)