Artificial Intelligence Applicability in the Pharmaceutical Industry: Present Perspectives and Future Formations.
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| Title: | Artificial Intelligence Applicability in the Pharmaceutical Industry: Present Perspectives and Future Formations. |
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| Authors: | Hasan, Maimuna1 (AUTHOR) maimuna@rpsu.edu.bd, Lamem, Md. Faiazul Haque1 (AUTHOR) Lamem.hoq@gmail.com, Sahid, Muaj Ibne1 (AUTHOR), Sarker, Md. Rifat1 (AUTHOR) |
| Source: | Inquiry (00469580). 3/9/2026, Vol. 63, p1-12. 12p. |
| Subject Terms: | *Documentation, *Artificial intelligence, *Decision making, *Research, *Automation, *Employee attitudes, Pharmaceutical industry & economics, Medication error prevention, Cross-sectional method, Data management, Descriptive statistics, Pharmaceutical industry, Professions, Organizational effectiveness, Data analysis software, Quality assurance, Drug development, Pharmacists' attitudes, Dependency (Psychology) |
| Abstract: | Artificial intelligence (AI), like ChatGPT, Microsoft copilot, and Google Gemini, is transforming pharmaceutical research, production, and healthcare delivery. Despite global adoption, the level of AI awareness, readiness, and application among pharmaceutical professionals remains unclear. This study aimed to evaluate the awareness, knowledge, attitudes, and perceptions of AI among pharmaceutical professionals and to identify factors influencing its adoption. A cross-sectional survey was conducted among professionals from various pharmaceutical sectors using a structured online questionnaire. Descriptive statistics were applied to analyze demographic characteristics, AI familiarity, usage, and perceptions. Most respondents (81.8%) were aware of AI, but only 2.3% reported extensive familiarity. ChatGPT was the most frequently used AI tool (63.6%), while only 27.3% reported organizational adoption. Support for AI adoption was high (86.4%), yet only 22.7% considered the industry fully ready. Perceived benefits included improved efficiency (86.4%) and quality (70.4%), while major concerns involved automation dependency (36.4%) and implementation costs (34.1%). Pharmaceutical professionals show strong interest in AI despite limited organizational readiness and formal training. Structured education, regulatory guidance, and ethical frameworks are critical for effective AI integration in the sector. [ABSTRACT FROM AUTHOR] |
| Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 192206345 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial Intelligence Applicability in the Pharmaceutical Industry: Present Perspectives and Future Formations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hasan%2C+Maimuna%22">Hasan, Maimuna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> maimuna@rpsu.edu.bd</i><br /><searchLink fieldCode="AR" term="%22Lamem%2C+Md%2E+Faiazul+Haque%22">Lamem, Md. Faiazul Haque</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Lamem.hoq@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Sahid%2C+Muaj+Ibne%22">Sahid, Muaj Ibne</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sarker%2C+Md%2E+Rifat%22">Sarker, Md. Rifat</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Inquiry+%2800469580%29%22">Inquiry (00469580)</searchLink>. 3/9/2026, Vol. 63, p1-12. 12p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Documentation%22">Documentation</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br />*<searchLink fieldCode="DE" term="%22Research%22">Research</searchLink><br />*<searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br />*<searchLink fieldCode="DE" term="%22Employee+attitudes%22">Employee attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmaceutical+industry+%26+economics%22">Pharmaceutical industry & economics</searchLink><br /><searchLink fieldCode="DE" term="%22Medication+error+prevention%22">Medication error prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Cross-sectional+method%22">Cross-sectional method</searchLink><br /><searchLink fieldCode="DE" term="%22Data+management%22">Data management</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmaceutical+industry%22">Pharmaceutical industry</searchLink><br /><searchLink fieldCode="DE" term="%22Professions%22">Professions</searchLink><br /><searchLink fieldCode="DE" term="%22Organizational+effectiveness%22">Organizational effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+assurance%22">Quality assurance</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+development%22">Drug development</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmacists'+attitudes%22">Pharmacists' attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Dependency+%28Psychology%29%22">Dependency (Psychology)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Artificial intelligence (AI), like ChatGPT, Microsoft copilot, and Google Gemini, is transforming pharmaceutical research, production, and healthcare delivery. Despite global adoption, the level of AI awareness, readiness, and application among pharmaceutical professionals remains unclear. This study aimed to evaluate the awareness, knowledge, attitudes, and perceptions of AI among pharmaceutical professionals and to identify factors influencing its adoption. A cross-sectional survey was conducted among professionals from various pharmaceutical sectors using a structured online questionnaire. Descriptive statistics were applied to analyze demographic characteristics, AI familiarity, usage, and perceptions. Most respondents (81.8%) were aware of AI, but only 2.3% reported extensive familiarity. ChatGPT was the most frequently used AI tool (63.6%), while only 27.3% reported organizational adoption. Support for AI adoption was high (86.4%), yet only 22.7% considered the industry fully ready. Perceived benefits included improved efficiency (86.4%) and quality (70.4%), while major concerns involved automation dependency (36.4%) and implementation costs (34.1%). Pharmaceutical professionals show strong interest in AI despite limited organizational readiness and formal training. Structured education, regulatory guidance, and ethical frameworks are critical for effective AI integration in the sector. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/00469580261422674 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Documentation Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Decision making Type: general – SubjectFull: Research Type: general – SubjectFull: Automation Type: general – SubjectFull: Employee attitudes Type: general – SubjectFull: Pharmaceutical industry & economics Type: general – SubjectFull: Medication error prevention Type: general – SubjectFull: Cross-sectional method Type: general – SubjectFull: Data management Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: Pharmaceutical industry Type: general – SubjectFull: Professions Type: general – SubjectFull: Organizational effectiveness Type: general – SubjectFull: Data analysis software Type: general – SubjectFull: Quality assurance Type: general – SubjectFull: Drug development Type: general – SubjectFull: Pharmacists' attitudes Type: general – SubjectFull: Dependency (Psychology) Type: general Titles: – TitleFull: Artificial Intelligence Applicability in the Pharmaceutical Industry: Present Perspectives and Future Formations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hasan, Maimuna – PersonEntity: Name: NameFull: Lamem, Md. Faiazul Haque – PersonEntity: Name: NameFull: Sahid, Muaj Ibne – PersonEntity: Name: NameFull: Sarker, Md. Rifat IsPartOfRelationships: – BibEntity: Dates: – D: 09 M: 03 Text: 3/9/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00469580 Numbering: – Type: volume Value: 63 Titles: – TitleFull: Inquiry (00469580) Type: main |
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