{"title":"从发现到交付:制药行业的人工智能治理","authors":"Stephanie Pasas-Farmer, Rashi Jain","doi":"10.1016/j.greeac.2025.100268","DOIUrl":null,"url":null,"abstract":"<div><div>Artificial Intelligence (AI) is revolutionizing the pharmaceutical industry, significantly enhancing drug discovery, patient care, and operational efficiency. Key AI technologies like machine learning, deep learning, natural language processing, and computer vision are transforming pharmaceutical practices. Despite the promising potential, AI implementation faces numerous challenges such as technical complexity, ethical concerns, regulatory hurdles, and a shortage of skilled professionals. Governance frameworks are essential to ensure AI technologies are ethically developed and deployed, balancing innovation with safety and transparency. Key components of AI governance include regulatory compliance, data governance, algorithm transparency, and continuous system monitoring. However, the fast pace of technological advancements, global regulatory discrepancies, and the need for stakeholder collaboration present ongoing challenges. Best practices for AI governance, such as promoting transparency, fostering multidisciplinary collaboration, and adhering to robust data management standards, are critical for ensuring the ethical and effective use of AI. Addressing these challenges will enable the pharmaceutical industry to fully harness the power of AI, ensuring patient safety and promoting innovation in healthcare.</div></div>","PeriodicalId":100594,"journal":{"name":"Green Analytical Chemistry","volume":"13 ","pages":"Article 100268"},"PeriodicalIF":6.2000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From discovery to delivery: Governance of AI in the pharmaceutical industry\",\"authors\":\"Stephanie Pasas-Farmer, Rashi Jain\",\"doi\":\"10.1016/j.greeac.2025.100268\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Artificial Intelligence (AI) is revolutionizing the pharmaceutical industry, significantly enhancing drug discovery, patient care, and operational efficiency. Key AI technologies like machine learning, deep learning, natural language processing, and computer vision are transforming pharmaceutical practices. Despite the promising potential, AI implementation faces numerous challenges such as technical complexity, ethical concerns, regulatory hurdles, and a shortage of skilled professionals. Governance frameworks are essential to ensure AI technologies are ethically developed and deployed, balancing innovation with safety and transparency. Key components of AI governance include regulatory compliance, data governance, algorithm transparency, and continuous system monitoring. However, the fast pace of technological advancements, global regulatory discrepancies, and the need for stakeholder collaboration present ongoing challenges. Best practices for AI governance, such as promoting transparency, fostering multidisciplinary collaboration, and adhering to robust data management standards, are critical for ensuring the ethical and effective use of AI. Addressing these challenges will enable the pharmaceutical industry to fully harness the power of AI, ensuring patient safety and promoting innovation in healthcare.</div></div>\",\"PeriodicalId\":100594,\"journal\":{\"name\":\"Green Analytical Chemistry\",\"volume\":\"13 \",\"pages\":\"Article 100268\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2025-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Green Analytical Chemistry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772577425000643\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Green Analytical Chemistry","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772577425000643","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
From discovery to delivery: Governance of AI in the pharmaceutical industry
Artificial Intelligence (AI) is revolutionizing the pharmaceutical industry, significantly enhancing drug discovery, patient care, and operational efficiency. Key AI technologies like machine learning, deep learning, natural language processing, and computer vision are transforming pharmaceutical practices. Despite the promising potential, AI implementation faces numerous challenges such as technical complexity, ethical concerns, regulatory hurdles, and a shortage of skilled professionals. Governance frameworks are essential to ensure AI technologies are ethically developed and deployed, balancing innovation with safety and transparency. Key components of AI governance include regulatory compliance, data governance, algorithm transparency, and continuous system monitoring. However, the fast pace of technological advancements, global regulatory discrepancies, and the need for stakeholder collaboration present ongoing challenges. Best practices for AI governance, such as promoting transparency, fostering multidisciplinary collaboration, and adhering to robust data management standards, are critical for ensuring the ethical and effective use of AI. Addressing these challenges will enable the pharmaceutical industry to fully harness the power of AI, ensuring patient safety and promoting innovation in healthcare.