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    "https://zenodo.org/records/18802221",
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  "dcterms:title": [
    "PHARMACEUTICAL SCIENCE IN THE ERA OFARTIFICIAL INTELLIGENCE"
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    "Journal article"
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  "dcterms:creator": [
    "Dhairya Gambhir*",
    "Kanishek Tiwari",
    "Govind Saini",
    "Himanshu Dhakad",
    "Keshav Yadav"
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  "dcterms:description": [
    "Purpose: This review discovers the transformative impression of Artificial Intelligence (AI) and Machine Learning (ML) on the pharmaceutical industry and healthcare delivery, focusing on drug discovery, clinical pharmacy practice, and operational productivity. Methods: The article inspects the historical development of AI, from early neural models to modern deep learning designs like GANs, RNNs, and Transformers, and assesses their specific requests transversely to the drug life span. Results: AI is publicised to significantly accelerate R&D by detecting drug leads quicker and adjusting clinical trials through patient-specific data analysis. In clinical settings, AI-driven choice support systems boost patient safety by reducing medication errors, predicting adverse reactions, and refining adherence—especially realising a 40% growth in adherence in community pharmacies. Still, technologies such as computer vision are restyling medicine supervision and analytical precision in medical imagination. Challenges: In spite of these benefits, the evolution characteristics sprints with \"black box\" interpretability, data privacy risks, algorithmic bias, and high implementation charges. Conclusion: This review highlights that despite the fact AI is redesigning pharmacy into an extra detailed and inventive field, its innocuous integration requires a specialised workforce exercise, strong governing agendas, and a constant emphasis on the vital social assembly in patient care."
  ],
  "dcterms:subject": [
    "Artificial Intelligence",
    "Technology",
    "Pharmaceuticals",
    "Drug Formulation",
    "Challenges"
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  "dcterms:language": [
    "en"
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    "CC-BY-NC-4.0 — assessed as OPEN_NC by the harvester's licence gate. Conditions: attribution required, non-commercial use only. open but Non-Commercial — SME/commercial reuse needs care"
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  "dcterms:issued": [
    "2026-02-27"
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  "dcterms:modified": [
    "2026-02-27"
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  "dcterms:created": [
    "2026-02-27"
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  "dcterms:bibliographicCitation": [
    "World Journal of Pharmaceutical Science and Research, vol. 5(3), pp. 105-122"
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      "@id": "https://doi.org/10.5281/zenodo.18802220"
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    "Retrieved from Zenodo on 2026-10-09 in response to the search string “(\"artificial intelligence\" OR \"machine learning\" OR \"generative AI\" OR \"deep learning\" OR \"reinforcement learning\" OR \"large language model\" OR \"AI\") AND (\"AI concepts\" OR \"types of AI\" OR \"AI fundamentals\" OR \"recognizing AI\" OR \"recognising AI\" OR \"general versus narrow AI\" OR \"narrow AI\" OR \"general AI\" OR \"machine intelligence\" OR \"AI strengths and weaknesses\" OR \"traditional software\" OR \"rule-based systems\" OR \"introduction to AI\" OR \"introduction to artificial intelligence\" OR \"artificial intelligence introduction\" OR \"AI primer\" OR \"foundations of artificial intelligence\" OR \"overview of AI\" OR \"understanding AI\" OR \"history of AI\" OR \"AI essentials\" OR \"AI terminology\" OR \"metaphors for AI\" OR \"AI fundamental concepts\" OR \"AI key concepts\" OR \"philosophy of AI\" OR \"critical AI literacy\")”. Zenodo served the resource and is not asserted to be its publisher or author.",
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