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 pr...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Green Analytical Chemistry |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772577425000643 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850192629675851776 |
|---|---|
| author | Stephanie Pasas-Farmer Rashi Jain |
| author_facet | Stephanie Pasas-Farmer Rashi Jain |
| author_sort | Stephanie Pasas-Farmer |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e5c6f5ad265c451d88bf6ca05f2797de |
| institution | OA Journals |
| issn | 2772-5774 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Green Analytical Chemistry |
| spelling | doaj-art-e5c6f5ad265c451d88bf6ca05f2797de2025-08-20T02:14:30ZengElsevierGreen Analytical Chemistry2772-57742025-06-011310026810.1016/j.greeac.2025.100268From discovery to delivery: Governance of AI in the pharmaceutical industryStephanie Pasas-Farmer0Rashi Jain1BioData Solutions, LLCCorresponding author at: 10 E. 9th Street, Suite D, Lawrence, Kansas 66044, USA.; BioData Solutions, LLCArtificial 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.http://www.sciencedirect.com/science/article/pii/S2772577425000643Artificial intelligencePharmaceutical industryAI governanceTransparencyAnalytical chemistryRegulatory challenges |
| spellingShingle | Stephanie Pasas-Farmer Rashi Jain From discovery to delivery: Governance of AI in the pharmaceutical industry Green Analytical Chemistry Artificial intelligence Pharmaceutical industry AI governance Transparency Analytical chemistry Regulatory challenges |
| title | From discovery to delivery: Governance of AI in the pharmaceutical industry |
| title_full | From discovery to delivery: Governance of AI in the pharmaceutical industry |
| title_fullStr | From discovery to delivery: Governance of AI in the pharmaceutical industry |
| title_full_unstemmed | From discovery to delivery: Governance of AI in the pharmaceutical industry |
| title_short | From discovery to delivery: Governance of AI in the pharmaceutical industry |
| title_sort | from discovery to delivery governance of ai in the pharmaceutical industry |
| topic | Artificial intelligence Pharmaceutical industry AI governance Transparency Analytical chemistry Regulatory challenges |
| url | http://www.sciencedirect.com/science/article/pii/S2772577425000643 |
| work_keys_str_mv | AT stephaniepasasfarmer fromdiscoverytodeliverygovernanceofaiinthepharmaceuticalindustry AT rashijain fromdiscoverytodeliverygovernanceofaiinthepharmaceuticalindustry |