Aug 2026· Reviews on recent clinical trials· 0 citations
Medicine
TL;DR
The findings suggest that the digitalization of the pharmaceutical industry and enhanced AI integration hold promise for developing safer and more effective therapeutic strategies, while also identifying obstacles to their widespread adoption and optimal functionality.
Abstract
The transformative role of artificial intelligence (AI) in the pharmaceutical industry is examined, with a focus on its significant contributions to drug discovery, development, and clinical trial processes. It highlights the inefficiencies and high costs associated with traditional drug development and explores how AI and machine learning (ML) can enhance these processes by analyzing extensive biological datasets. The historical context of AI in pharmaceutical development is examined, noting how advances in computational power and data accessibility have facilitated innovative methodologies, such as predictive analytics and natural language processing. Contemporary trends reveal the integration of AI technologies in drug design, repurposing, and patient response forecasting. This study also addresses the challenges of participant recruitment for clinical trials and proposes AI-driven solutions to optimize patient selection and data management. Furthermore, it discusses AI's role in tailored medicine, emphasizing its potential for advancing precision therapy through targeted drug development and personalized treatment strategies. The importance of digital tools, genomic data analysis, and AI-driven imaging technologies for customizing therapeutic approaches is underscored, along with the regulatory and ethical challenges posed by AI deployment in healthcare. This study illustrates the complexities of AI applications in the pharmaceutical sector, offering insights into both successful and unsuccessful initiatives. The findings suggest that the digitalization of the pharmaceutical industry and enhanced AI integration hold promise for developing safer and more effective therapeutic strategies, while also identifying obstacles to their widespread adoption and optimal functionality.
Artificial intelligence (AI) is changing the pharmaceutical industry at every stage of drug development. This review examines how AI is currently used and the recent progress in areas such as manufacturing, drug discovery, formulation, clinical trials, and regulatory work. It covers topics such as predictive maintenanc...
Andersson F. Montaña, Jorge A. Mora, J. Martínez et al.· BioMedInformatics· 0 citations
The analysis suggests that AI will become an increasingly important component of pharmaceutical workflows; however, long-term impact will depend less on algorithmic advancement alone and more on effective integration with biological validation, experimental rigor, clinical evidence, and scalable translational infrastru...
Andrew Matelis· American Journal of Student...· 0 citations
Challenges such as data quality, algorithmic bias, lack of transparency, privacy, cybersecurity, regulatory uncertainty and the need for human oversight remain important.
Rajashree Somnath Chorgade, Srushti Dipak Nevase, Shubham Hanumant Dhavale et al.· International Journal of Inn...· 0 citations
The evolving role of AI in modern drug discovery is discussed while highlighting the importance of explainable algorithms, high-quality biomedical data, real-world evidence, and interdisciplinary collaboration.
Mohsen Zabihi· Advances in Pharmacology and...· 0 citations
The many advantages of AI in drug development are highlighted in this paper, including increased accuracy, reduced risks, and increased efficiency, including increased accuracy, reduced risks, and increased efficiency.
{"name":"Shaik Sana","email":"shaiksana79313@gmail.com","affiliat, {"name":"Sarvepalli Revathi","email":"shaiksana79313@gmail.com","affil, {"name":"Yerikala Ramesh","email":"shaiksana79313@gmail.com","affili et al.· Future Journal of Pharmaceut...· 0 citations
Although challenges related to data governance, interoperability, ethical oversight, and workforce preparedness remain, responsible AI adoption supported by robust regulatory frameworks will be critical for sustaining long-term value.
Upendra Nagaich· Journal of Pharmaceutical Te...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.