Accelerating the development of new quality productivity is the proper meaning of high-quality development, and it is a strategic choice to seize the commanding heights of a new round of global scientific and technological revolution and industrial transformation, open up new fields and new tracks for development, cultivate new momentum for development, and enhance new competitive advantages. Today, when the country proposes to accelerate the development of new quality productivity, generative AI has received more and more attention as an emerging breakthrough technology, which has brought revolutionary changes to the medical and pharmaceutical industry. Recently, EY released the report "Smart New Quality Productivity: Potential Applications of Generative AI in the Healthcare and Pharmaceutical Sector", which explores the integration of generative AI and traditional AI, its application in the medical field, the potential of empowering doctor-patient and pharmaceutical companies, and the challenges and coping strategies under policy supervision.
From "smart device" to "smart brain": generative AI achieves multi-layered capability progression
The rise of generative AI has brought unprecedented creativity and imagination. Traditional AI embodies the attributes of smart tools ("smart tools") that focus on specific tasks and have powerful data processing and analysis capabilities, allowing many business processes to be automated and improve work efficiency.
Compared to traditional AI, generative AI has shown great potential in terms of smart brains ("smart brains"). It has demonstrated transformative power in content generation, intelligent interaction, data analysis and prediction, and decision support. In the process of leapfrogging, generative AI presents several major capabilities that have progressed from structured processing, to specialized support, and then to interactive generation.
Convergence Scenario: Deeply Impacting the "Diagnosis and Treatment Journey"
The medical field is one of the important application fields of generative AI, which can empower patients with the complete link from diagnosis and treatment to post-recovery management, from disease understanding and disease treatment to post-recovery health management. On the one hand, it empowers hospitals and doctors, helps doctors release more work efficiency, improves medical quality, and alleviates the problem of medical resource constraints, and on the other hand, brings more accurate, convenient and personalized medical services and health support to patients.
- In terms of disease understanding, generative AI can provide patients with personalized disease education content through knowledge presentation or interactive Q&A, such as intelligent voice assistants or virtual assistants, so that patients can get disease-related information and answers anytime and anywhere.
- In terms of disease diagnosis and treatment, the field of medical imaging is currently a relatively mature field for artificial intelligence applications, and generative AI can often use deep learning technology to analyze medical images to help doctors diagnose diseases faster and more accurately.
- In terms of post-recovery health management, generative AI can provide timely reminders or early warnings to help formulate personalized health management plans and improve patients' self-management ability and compliance.
Empowering enterprises: Supporting the front, middle and back office operations of pharmaceutical companies
In the operation scenario of pharmaceutical companies, generative AI is changing the traditional drug development and production model, providing support for the front, middle, and back office operations of pharmaceutical companies. In the front-end functions, generative AI empowers patient services, marketing market analysis, corporate strategic planning and other fields, providing pharmaceutical companies with more accurate business planning and operational optimization.
In the middle and back office, the core applications of generative AI will include drug development, manufacturing, and supply chain optimization, which will play a role in accelerating the process of bringing new drugs to market, reducing costs and increasing efficiency. At the same time, generative AI can also help with production automation, quality control, and IT operations to improve productivity and ensure stable operations.
Challenges, risks and solutions
The report points out that in the application of generative AI, large language models have become the core technology to drive enterprise innovation. Compared with general large models, the value of domain large models lies in their ability to provide more accurate predictions, deeper analysis, and more effective decision support, so as to achieve automated and intelligent business operations in specific domains. Security and compliance maintenance is a critical step in the implementation of large models. The report highlights that as the model continues to run, organizations must always pay attention to data security and privacy protection. All operations must comply with legal regulations and relevant standards.
At present, local governments have issued a series of policies and regulations to create an environment for the development of generative AI on the one hand, and to comprehensively regulate generative AI on the other. Compliance challenges in data, content, user management, and more continue to escalate. At the same time, the training process of large models also faces various data-related challenges. From data collection to the training and deployment of large models, how to carry out comprehensive quality control and risk management, how to find effective methods to control the cost of annotation processing, and how to improve the transparency and interpretability of decision-making through visualization and explanatory algorithms, etc., are all important issues faced by enterprises. How to find countermeasures, the report suggests that relevant enterprises need to follow up in a timely manner, grasp relevant policies such as artificial intelligence, data governance, and cybersecurity, and combine the characteristics of the medical and pharmaceutical fields to prevent, identify and avoid risks in a timely manner.
Content Source | "Zhiqi New Quality Productivity: Potential Applications of Generative Artificial Intelligence in the Medical and Pharmaceutical Field", Big Health School
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