Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0338
version: 5
kind: sources_changed
reason: Source set updated
timestamp: 2026-10-16T06:55:27.933744Z
status: published
lens: g_space
sector: health
headline: Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine
dek: Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug developm…
gain_title: AI techniques including machine learning and deep learning accelerate drug discovery and delivery and improve patient outcomes and treatment regimens in personalized medicine.
problem_title: (none)
trace_subject: (none)
gain_reading: AI techniques including machine learning and deep learning accelerate drug discovery and delivery and improve patient outcomes and treatment regimens in personalized medicine.
gain_evidence: acceleration of drug discovery and delivery | improvement of patient outcomes | enhance efficiency, reduce costs, and improve both medicines and patient health
problem_reading: (none)
problem_evidence: (none)
quick_read: This peer-reviewed review from October 2024 surveys how AI techniques such as machine learning and deep learning are applied across pharmaceutical development, including target identification and validation, excipient selection, synthetic route prediction, supply chain optimization, and continuous manufacturing monitoring.

It matters because it connects those technical applications to health outcomes like faster drug discovery and delivery and improved patient health, while acknowledging that regulatory oversight questions remain unresolved as of the publication date.
limitation: 
tag: Evidence-backed gain
key_points: Article describes AI encompassing machine learning, deep learning, and other advanced computational methods used by pharmaceutical companies for decades. | Lists specific pharmaceutical applications: target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, and predictive maintenance. | Frames review scope as covering drug discovery, target optimization, personalized medicine, and drug safety with analysis of research trends and case studies.
rundown: The review positions AI as swiftly transforming the pharmaceutical industry from discovery through manufacturing, citing long-standing use of machine learning and deep learning alongside newer computational methods.

By publication date 2024-10-14, the article presents observed opportunities and industry adoption rather than a single trial result, noting efficiency and cost benefits while flagging regulatory questions as an open issue.
sources:
- peer_reviewed | Current Issues in Pharmacy and Medical Sciences | https://doi.org/10.12923/cipms-2025-0020 | 2025-05-21
- peer_reviewed | Innovations in Digital Health, Diagnostics, and Biomarkers | https://doi.org/10.36401/iddb-24-x2 | 2024-07-02
- peer_reviewed | International Journal for Research in Applied Science and Engineering Technology | https://doi.org/10.22214/ijraset.2025.74553 | 2025-10-12
- peer_reviewed | International Journal of Pharma Professional's Research (IJPPR) | https://doi.org/10.69580/ijppr.16.2.2025.62-85 | 2025-04-30
- peer_reviewed | Pharmaceutics | https://doi.org/10.3390/pharmaceutics16101328 | 2024-10-14
prev: 1b51e47c0cad6f65e0100383638639df9e9282584516e49a7a62d3061e238d9b

sha256

b81a5a90d240305622303f04c640121407a912ae5e9e168cf8a525d77f98a587

previous

1b51e47c0cad6f65e0100383638639df9e9282584516e49a7a62d3061e238d9b