AI in Pharma: Global Impact, Future Models, & Ethical Oversight

Generative AI is profoundly transforming the medical field by optimizing patient-doctor experiences and business operations through intelligent consulting and precise marketing; intelligent data processing and pattern recognition deeply mine biomedical data,automatically discover candidate drug targets,and predict the pharmacokinetic properties and toxicity of drugs,improving efficacy and safety assessments; in supply chain management,generative AI uses data analysis to predict and optimize procurement,order,and logistics processes,increasing the accuracy of demand forecasting and achieving procurement automation.

With the rise of generative artificial intelligence (generative AI),its technological capabilities are redefining the boundaries of traditional AI.It has not only made a qualitative leap at the technical level but also demonstrated unique creativity and imagination in the medical and pharmaceutical industries.

Generative AI provides new perspectives and methods for disease diagnosis and treatment through deep learning and pattern recognition.It optimizes the diagnosis and treatment process,improves the quality and efficiency of medical services,and also brings a more personalized and humanized experience to patients.The application of this technology is gradually changing the face of the medical industry,providing unprecedented convenience for patients and doctors.

01

The Transformation of AI: From Knowledge Transmitter to Generator and Creator

The rise of generative AI poses a disruptive challenge to traditional AI.The development from traditional AI to generative AI is a process that involves both evolution and rebirth,with both distinctions and integrations between the two.

The difference between traditional AI and generative AI lies in their working objectives,as well as their attitudes and methods towards knowledge.Traditional AI focuses more on the application and reasoning of existing knowledge.Its goal is to answer specific questions or solve specific tasks,and its working method is more like the transmission of knowledge.Therefore,traditional AI reflects the attributes of intelligent tools,that is,"intelligent devices," which have strong data processing and analysis capabilities,allowing many business processes to be automated and improving work efficiency.

Generative AI,on the other hand,focuses more on generation and creation.Its goal is to generate new,authentic,and useful data and content,and its working method is more like the induction and deduction of knowledge.This distinction allows them to play important roles in their respective fields and provide infinite possibilities for future development.Generative AI is more like an intelligent brain,that is,"intelligent brain." Its creativity,versatility,and flexibility make generative AI widely applicable in content creation,virtual character generation,and other areas.

At the same time,the factors affecting the quality of the content generated by the two are different.Traditional AI has pain points in data scale,hardware costs,deployment complexity,and data dependency,and the quality of the generated content is highly dependent on hardware performance and data capabilities.In contrast,the quality of the input questions,including whether they are accurate,focused,and structured,is an important factor affecting the quality of content generation.Opportunities in Patient-Doctor Scenarios: Enhancing Patient Experience and Treatment Efficiency

With the advancement of algorithms,computational power,and data capabilities,generative AI is profoundly transforming the medical field.Returning to the essence of serving human society,generative AI plays a crucial role throughout the patient's medical journey,from symptom perception and awareness to disease diagnosis and treatment,follow-up visits and medication refills,and post-recovery management.

In the initial stage,patients may have insufficient understanding of their symptoms and could overlook early signs of diseases.At this point,generative AI,through intelligent interaction and personalized disease education content,helps patients better understand their symptoms and potential diseases.Moreover,with the help of intelligent voice assistants or virtual assistants,patients can access disease-related information and answers to their questions anytime,anywhere,to improve their understanding of their conditions.

During the diagnosis and treatment phase,AI technology can analyze medical images through deep learning,enhancing the accuracy of diagnoses and assisting doctors in formulating precise treatment plans based on the analysis results.

For follow-up visits and medication refills,the intelligent reminder function of generative AI ensures that patients receive treatment and medication on time,avoiding the loss of crucial treatment opportunities.At the same time,intelligent pharmacy management technology can automatically allocate medication based on the patient's prescription information,enabling patients to quickly and accurately obtain the required drugs.

In the post-recovery phase,AI not only improves patients' health management through regular follow-ups and vital sign monitoring but also provides personalized health management plans and educational content,helping patients improve their quality of life and self-management abilities.Overall,the application of generative AI makes the patient's medical process more intelligent,efficient,and humanized,greatly enhancing the quality of medical services and patient satisfaction.

By reaching diverse scenarios from different perspectives of patients and doctors,generative AI can also make diagnosis and treatment "higher quality," doctors "more professional," and patients "more autonomous." Traditional diagnostic methods often rely on doctors' clinical experience,centering around the doctor.Generative AI can analyze a large amount of clinical diagnostic data,deeply learn medical knowledge,and provide doctors with more accurate and reliable diagnostic evidence.This not only improves the accuracy of diagnoses but also helps doctors better formulate treatment plans,especially assisting doctors with limited clinical experience and in areas with scarce medical resources.On the other hand,the combination of new technology with patients' disease information can bring more precise,convenient,and personalized medical services and health support,enhancing patients' self-awareness and improving innovative interactive experiences.

03

Empowering the Operational Scenarios of Pharmaceutical Companies: Long-term Enhancement of "Front-Middle-Back" CoreIn various stages of pharmaceutical company operations,generative AI is playing an increasingly important role,not only transforming traditional drug research and development and production models but also providing strong support for the front,middle,and back office operations of pharmaceutical companies.

Generative AI empowers the front office with "generation" itself,playing a key role in the front office functions of pharmaceutical companies,including patient services,market analysis,and corporate decision-making planning.It optimizes patient and doctor experiences and business operations through intelligent consulting and precise marketing.For example,by automatically generating market research questionnaires,business reports,and targeted marketing plans,it accurately locates customers and improves the efficiency of marketing and sales departments.In addition,generative AI can greatly improve work efficiency and decision-making quality in market access,project management,and compliance checks,thus promoting corporate operations to be more efficient,accurate,and customer-friendly.

The application of generative AI in strategic planning and operational optimization can significantly improve efficiency and strategic value.By automatically generating market research,sales reports,and training materials,it frees up employee time,allowing them to focus on customer insights and strategic innovation.Secondly,automated customer communication can improve satisfaction and loyalty,and optimize products and services through data analysis.Moreover,in the medical department,generative AI can accelerate medical research and content generation,improve work efficiency,and support the learning and communication of Medical Science Liaison (MSL).

Generative AI can solidify the middle and back office with "excellent data".Its application in the R&D department will bring profound changes to drug research and development.Its intelligent data processing and pattern recognition capabilities can deeply mine biomedical data,automatically discover drug targets,and predict the pharmacokinetic properties and toxicity of drugs,thereby improving the efficacy and safety assessment of drugs.In the clinical trial stage,AI can improve the success rate of trials by optimizing trial design and improving patient screening efficiency.In addition,it can automatically organize and analyze registration application materials to accelerate the drug listing process.

Generative AI in the IT department can improve response speed and generate high-quality code through automated customer support,enhance IT service experience,accelerate project implementation,and reduce human errors.In production and quality management,generative AI can optimize production processes and quality control by identifying production bottlenecks and predicting equipment failures,ensuring production stability and continuity.In supply chain management,generative AI can predict and optimize procurement,order,and logistics processes through data analysis,improve demand forecasting accuracy,and achieve procurement automation.

Generative AI will bring more intelligent,efficient,and reliable supply chain management to the supply chain department,enhancing its core competitiveness.With its powerful data processing and predictive analysis capabilities,the technology provides new optimization methods for procurement,order,and logistics management in the supply chain.By deeply analyzing historical cases and market trends,generative AI is expected to improve the accuracy of demand forecasting.It can formulate more reasonable procurement plans and cost optimization strategies by analyzing historical supplier prices and delivery times.In the procurement process,generative AI can achieve automated identification of procurement needs,automatic matching of suppliers,and automatic generation of contracts,improving procurement efficiency and reducing human errors.At the same time,the technology can monitor and analyze supplier delivery performance,product quality,and contract fulfillment to help them identify potential problems and take corresponding measures in a timely manner.In logistics distribution,the technology can also assist in decision-making on the optimal transportation routes,improving logistics efficiency and reducing transportation costs.

In the financial department,generative AI can improve financial management level through data analysis,suspicious transaction prediction,and automated report generation,supporting financial decision-making and improving work efficiency.In human resources,AI supports talent recruitment and management by efficiently screening resumes,summarizing interview feedback,and automatically generating entry contracts,and provides employee performance insights to support personalized training and development plans.The legal and compliance department can identify key information in documents,generate compliance reports,and automate contract review through the application of generative AI,improving legal work efficiency and supporting risk management.In public relations and communication,AI can enhance brand image and communication effectiveness through precise public opinion monitoring,automated crisis response,and generation of high-quality promotional materials.

04

Six key steps to successfully implement scenario-based landingThe exploration and application of generative AI by existing enterprises have made large language models the core technology driving corporate innovation.After deep learning and training with massive amounts of data,these models can understand natural language and also generate language,providing intelligent solutions for multiple industries.Implementing large language models is a complex process that involves strategic planning,technical deployment,application implementation,and continuous optimization.The practical path to implementing these models mainly consists of six steps.

Firstly,there is an in-depth analysis of business needs.Before adopting large language models,enterprises must thoroughly examine their own business needs.This includes a comprehensive understanding of data structures,business processes,user interactions,and technical infrastructure.Based on this analysis,companies need to set clear objectives,expected outcomes,risk assessments,and financial budgets.

Secondly,there is strategic technology selection.Faced with a multitude of large language models,companies need to conduct a detailed evaluation to choose the model that best matches their business needs.This decision-making process needs to consider performance,cost,scalability,user-friendliness,and data security among other dimensions.At the same time,companies also need to decide whether to use external cloud services or build their own models.

Thirdly,there is the preparation and cleaning of data.High-quality data is key to the success of large language models.Enterprises must invest resources in data collection,annotation,and formatting to ensure the accuracy and compliance of the data while also protecting data privacy.

Fourthly,there is application development and testing.To ensure that large language models play the greatest role in specific business scenarios,companies need to develop customized applications,such as chatbots and smart assistants.Continuous testing during the development process is an important part of ensuring that the application performance meets expectations.

Fifthly,there is employee training and change management.The introduction of new technology requires the adaptation and acceptance of employees.Therefore,providing targeted training and effective change management strategies are crucial for the successful application of new technology.

Sixthly,there is continuous attention to security and compliance.During the operation of large language models,companies must continuously monitor data security and privacy protection to ensure that all operations comply with laws,regulations,and international standards.

Building domain-specific large models offers more practical help for enterprises.Domain-specific large models refer to large artificial intelligence models trained specifically for certain industries or domains,which exhibit higher professionalism and accuracy within their professional fields.These models can provide more accurate predictions,in-depth analysis,and effective decision support,promoting automation and intelligence in specific areas for companies.

In the process of building domain-specific large models,since existing pre-trained language models have laid a solid foundation,the next step is to make these models better adapt to specific tasks or absorb domain knowledge for further optimization.The mainstream strategies to achieve this optimization are mainly divided into two types: Retrieval-Augmented Generation (RAG) and Fine-tuning.The RAG technology is a method that combines Retrieval and Generation.It first searches a large unstructured knowledge base to find content related to the input,then feeds these contents along with the original input into the generation model,and finally outputs the result.The advantage lies in the accuracy of information and richness of knowledge,but the disadvantages are high complexity and slow processing speed.

Finetuning technology,on the other hand,optimizes model performance by continuing training on specific tasks,such as SFT (Supervised Finetuning) and DPO (Direct Preference Optimization),to achieve more accurate predictions and analysis.The advantage is fast processing speed and significant performance improvement,but the disadvantage is high data requirements and higher update costs.

05

Policy Challenges and Responses in the Exploration Process

With the rapid development of generative artificial intelligence technology,governments around the world are actively formulating relevant policies to promote the healthy development of this field.For example,cities like Shanghai and Shenzhen have provided clear development directions and support for local generative AI industries through local regulations such as the "Shanghai Artificial Intelligence Industry Development Regulations" and the "Shenzhen Special Economic Zone Artificial Intelligence Industry Promotion Regulations".Beijing has made in-depth planning and layout in the field of artificial intelligence innovation by issuing the "Beijing Artificial Intelligence Innovation Source Construction Implementation Plan (2023-2025)" and the "Beijing Measures to Promote the Innovative Development of General Artificial Intelligence".

Nevertheless,the robust development of generative artificial intelligence also requires effective risk management.Only by combining industry characteristics and formulating and implementing risk response strategies in a timely manner can we ensure the continuous progress and healthy development of artificial intelligence in the medical and pharmaceutical fields.

Training generative AI models requires a large amount of high-quality data as a foundation.The acquisition and analysis of these data are crucial for forming effective AI results,and this process will face various challenges related to data.

Firstly,the quality and diversity of training data affect performance.The quality and diversity of training data directly affect the performance and accuracy of AI models.Bias or quality issues may lead to inaccurate AI results.Therefore,comprehensive quality control and risk management measures need to be implemented from the data collection stage.

Secondly,there is the issue of cost for new data annotation and processing.Collecting new data and annotating and processing it is a cost-intensive task that requires a lot of resources and time.To reduce costs,automated annotation techniques can be used,and open-source datasets can be utilized,while ensuring data accuracy and diversity through data cleaning,annotation,and augmentation methods.

Thirdly,there is the issue of decision transparency and interpretability.The decision-making process of generative AI often lacks transparency,which limits people's understanding of the logic behind it.To enhance the credibility and user acceptance of AI,the transparency and interpretability of the decision-making process need to be improved through visualization tools and explanatory algorithms.The medical and pharmaceutical industry,due to its importance to human health and life safety,has long been subject to strict regulatory compliance.With the advancement of anti-corruption in healthcare,regulatory measures have been continuously strengthened.Artificial intelligence,as an emerging strategic industry,has also been highly valued by government departments,leading to the introduction of multiple regulations,represented by the "Interim Measures for the Management of Generative Artificial Intelligence Services" (hereinafter referred to as the "Interim Measures"),to comprehensively regulate generative AI.The overlap of compliance regulation in the two major fields of artificial intelligence and medical pharmaceuticals presents compliance challenges that pharmaceutical companies must face when using generative AI.

Firstly,there is an increased emphasis on content compliance regulation.Generative AI has immense value in content empowerment in the medical and pharmaceutical fields,and behind the creation of a vast amount of content lies many compliance risks.With the implementation of the "Interim Measures",the standards for content compliance regulation have been raised,requiring generated content not only to align with the core values of socialism but also to avoid generating illegal and undesirable information.Additionally,the "Regulations on the Management of Algorithmic Recommendations for Internet Information Services" require companies to establish a feature library for identifying illegal and undesirable information,enhancing their ability to recognize such content.

Secondly,regulation of user management compliance is beginning to be strengthened.The training of generative AI requires processing a large amount of multimodal data that includes sensitive user information.To ensure the privacy and security of users,regulations such as the "Interim Measures" stipulate strict data management requirements.This includes legally collecting user information,adhering to the principle of minimization,establishing a clear privacy policy and obtaining user consent,as well as setting up complaint and reporting mechanisms to quickly respond to user demands.These measures aim to fully protect the legitimate rights and interests of users when using AI services.

Thirdly,there is compliance regulation of data in the medical and pharmaceutical industry.When applying generative AI in the medical and pharmaceutical field,its decision-making opacity may pose challenges to intellectual property rights,data security,and ethics.According to the "Regulations on the Administration of Human Genetic Resources of the People's Republic of China",before collecting genetic resources,it is necessary to inform the purpose,use,health impact,and privacy protection measures,and conduct ethical reviews to ensure compliance and the protection of individual rights.

In a complex market environment,companies always need to have sufficient resilience and wisdom to face challenges and rise against the trend.The Chinese government has shown a high degree of foresight and adaptability in regulatory aspects.The formulation of regulations such as the "Interim Measures for the Management of Generative Artificial Intelligence Services" not only provides a legal framework for the healthy development of artificial intelligence technology but also points out the path of compliant operation for related companies.Companies must strictly comply with laws and regulations when using artificial intelligence for research and innovation,ensuring the legality and safety of technology applications.In addition,companies should also closely monitor policy changes and adjust their research and development strategies and business models in a timely manner to adapt to the dynamic development of the regulatory environment.

On January 31,2024,for the first time,the country comprehensively and systematically explained the important concept of new quality productive forces,emphasizing "originality" and "disruptiveness" as the "core elements of developing new quality productive forces" when emphasizing technological innovation.This is both an original breakthrough from "0 to 1" and a disruptive leap from "1 to 10".As an emerging innovative breakthrough technology that possesses both characteristics,generative AI will show more brilliant innovative ideas to empower various industries.

We also look forward to generative AI further empowering various scenarios in the medical and pharmaceutical industry,which combines tradition and innovation,to achieve better patient-doctor experiences and more efficient internal operations of pharmaceutical companies,thereby promoting the high-quality development of the industry in the future.