ReviewPharmacological reports : PR2023
Opportunities and challenges in application of artificial intelligence in pharmacology.
Review in Pharmacological reports : PR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Photonic and nanomaterial-driven optical biosensing strategies for Parkinson's disease.Mikrochimica acta · 2026Review
- Progress in targeted therapy for prostate cancer via cell surface proteins (Review).Biomedical reports · 2026Review
- AgentMol: Multi-Model AI System for Automatic Drug-Target Identification and Molecule Development.Methods and protocols · 2025Article
- A Comparison of AI and Population PK Models to Predict the Concentrations of Antiepileptic Drugs Using Therapeutic Drug Monitoring Records.Clinical and translational science · 2025Article
- Comprehensive Global Analysis of Future Trends in Artificial Intelligence-Assisted Veterinary Medicine.Veterinary medicine and science · 2025Article
- Bridging the gap: From petri dish to patient - Advancements in translational drug discovery.Heliyon · 2025Review
- Preeclampsia prediction and diagnosis: a comprehensive historical review from clinical insights to omics perspectives.Frontiers in medicine · 2025Review
- Attitudes toward artificial intelligence and robots in healthcare in the general population: a qualitative study.Frontiers in digital health · 2025Article
- Hybrid fragment-SMILES tokenization for ADMET prediction in drug discovery.BMC bioinformatics · 2024Article
- In Silico Pharmacology for Evidence-Based and Precision Medicine.Pharmaceutics · 2023Article
- Perspective on the challenges and opportunities of accelerating drug discovery with artificial intelligence.Frontiers in bioinformatics · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
Indexed as
Identifiers
What Socratic 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 Socratic graph.