Adapting artificial intelligence into the evolution of pharmaceutical sciences and publishing: Technological darwinism
Open accessAbstract
This article explores the application of Artificial Intelligence (AI) in pharmaceutical research and development, particularly in the use of language models such as Chat Generative Pre-Trained Transformer (GPT). The evolution of technology and scientific publishing is discussed, highlighting the need for adaptability and change. The advancements in pharmaceutical science over the years are outlined, including the contribution of techniques such as combinatorial chemistry and pharmacogenomics. The potential benefits of AI models in pharmaceutical research and development are explored, such as assisting in literature reviews, data analysis, modeling, and interpretation, as well as aiding in the drug discovery and development processes. However, the limitations and potential biases of these tools are also recognized, and the importance of using them appropriately and in conjunction with human expertise and critical thinking is emphasized. The article also discusses the need for pharmaceutical researchers and practitioners to carefully evaluate the output of AI models, checking for accuracy and potential biases, and to incorporate this information into their published work thoughtfully. Guidelines and policies regarding the use of AI models in scientific publications are suggested, highlighting the importance of rigor and transparency in research. Authors are encouraged to stay up-to-date with current research and guidelines and collaborate closely with journal editors and reviewers to ensure appropriate and transparent use of AI models. The article concludes with a reflection on the ongoing evolution of technology and the need for researchers to stay updated with the latest advancements and best practices to succeed. The importance of adaptability and change is highlighted, and Charles Darwin's theory of evolution and natural selection is used to emphasize the relevance of this concept in the context of AI and pharmaceutical research. Overall, the article presents a thought-provoking and informative discussion of the potential benefits and limitations of AI language models in pharmaceutical research and development and the need for appropriate and effective use of these tools.
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AI models may not be able to adapt quickly enough to dynamic environments, leading to sub-optimal performance and potential errors.
“AI models may not be able to adapt quickly enough to these dynamic environments, leading to sub-optimal performance and potential errors [98].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [98]
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Cited by
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“AI models may not be able to adapt quickly enough to these dynamic environments, leading to sub-optimal performance and potential errors [98].”
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“Finally, and perhaps most important, it is still difficult for ChatGPT to completely achieve critical thinking like humans (Davies, 2023).”
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“[30], [35], [38], [40], [41], [43], [46], [47], [51], [55], [57], [61], [63], [67], [72], [77], [79]–[81], [85], [89], [104]–[113] that is extremely helpful to facilitate literature review, detecting research gaps and construct hypothesis in a timely manner[10], [14], [16], [24], [46], [47], [50], [62]–[64], [67], [72…”
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“Here, we comprehensively examined how ChatGPT enhances the efficiency of drug delivery [8].”
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Richter, L. M., Al-Gousous, J., de Araújo, G. L. B., Davies, N. M., & Löbenberg, R. (2024) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2023.105305 · Cited by 6 · see citing works
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Iglesias‐Puzas, Á., Conde‐Taboada, A., & López‐Bran, E. (2023) · Journal of Healthcare Quality Research · doi:10.1016/j.jhqr.2023.09.002
Consideraciones sobre el uso de ChatGPT en la práctica médica
Iglesias‐Puzas, Á., Conde‐Taboada, A., & López‐Bran, E. (2023) · Journal of Healthcare Quality Research · doi:10.1016/j.jhqr.2023.09.007
El‐Dahiyat, F., Refae, G. A. E., Jairoun, A. A., Al‐Hemyari, S. S., Shahwan, M. J., Alshehri, F. S., & Alorfi, N. M. (2024) · Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE) · doi:10.1109/gdigihealth.kee62309.2024.10761615
Bantu, R., Phukan, S., & Haydar, S. N. (2025) · Molecular Diversity · doi:10.1007/s11030-025-11235-1 · Cited by 1 · see citing works
- Authors
- Davies, N. M.
- Affiliations
- University of Alberta (CA)
- Corresponding author
- Davies, Neal M.
- ORCID
- Davies, Neal M.
- Venue
- Journal of Pharmacy & Pharmaceutical Sciences
- Publisher
- Frontiers Media SA
- Published
- 2023-03-22
- Volume
- 26
- Pages
- 11349-11349
- ISSN
- 1482-1826 (ISSN-L)
- PMC
- PMC10075305
- OpenAlex
- W4353092806
- Cited by
- 17
- References
- 0
- Citations by year
- 2025: 3 · 2024: 9 · 2023: 5
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Pharmacy & Pharmaceutical Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- scholarly publishing, algorithmic bias, drug discovery, literature review automation, combinatorial chemistry, pharmacogenomics, research transparency, technological change
- MeSH terms
- Artificial Intelligence, Drug Industry, Pharmaceutical Preparations, Publishing
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