ReviewCurrent pharmaceutical biotechnology2026
Artificial Intelligence Revolution in Pharmaceutical Sciences : Advancements, Clinical Impacts, and Applications.
Review in Current pharmaceutical biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Neuro-Transcriptomic Responses to Polypharmacological Agents inBrain sciences · 2026Article
- The Molecular Architecture of Neurodegeneration: An Integrative Overview of Convergent Mechanisms.NeuroSci · 2026Review
- From microfluidics to nanodelivery: artificial intelligence reshapes neuropharmacology research strategies.Frontiers in pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is a rapidly transforming drug discovery and development process, significantly impacting the pharmaceutical industry and enhancing human health. This review article examines the tremendous role of AI in analyzing complex biological data, optimizing research processes, and reducing costs of production. Implementation of AI in the pharmaceutical sector can store a vast dataset of manufacturing processes, identify potential disease targets, simulate physiological conditions, and predict drug interactions. The review article also discusses the AI concepts and their applications, particularly in developing solid dosage forms. Advanced algorithms optimize formulation processes, predict pharmacokinetics profiles, and assess drug toxicity profiles, facilitating a more efficient pathway from pilot study to market. Additionally, this review highlights the advancements in 3D printing technologies of dosage forms that have the ability to provide personalized treatment to different individuals. Furthermore, the article explores the opportunities and challenges of AI in healthcare, focusing on applications such as disease diagnosis, digital therapy, and epidemic forecasting. Prominent AI technologies like deep learning and neural networks are examined for their roles in predicting outbreaks of diseases like influenza and COVID-19. As the pharmaceutical landscape evolves, AI is poised to redefine traditional methods. This paves the way for more efficient healthcare solutions. By harnessing the interplay of technology and science, AI not only increases productivity; but it also promotes a new era of precision medicine tailored to the needs of each patient.
Indexed as
Identifiers
39851128What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.