ReviewFrontiers in public health2025
Exploring the ethical issues posed by AI and big data technologies in drug development.
Review in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Training the next-generation of biomedical scientists through artificial intelligence-driven education and research in pharmacology and pharmaceutical sciences.Experimental biology and medicine (Maywood, N.J.) · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development and wide application of Artificial Intelligence (AI) and Big Data technologies have profoundly changed the way industries around the world operate, from finance, transportation, education to media, the integration of the two not only improves the efficiency of the industry, but also optimizes the quality of service and decision-making process to a large extent. In the era of deep integration of Biomedicine and AI, AI and Big Data technology are reconstructing the paradigm of drug development with unprecedented intensity. The long cycle of traditional drug development, which takes a decade and billions of dollars in investment, is being compressed to 2 years or even less under the drive of AI. Through big data analytics and deep learning techniques, AI can greatly improve R&D efficiency and accuracy in a variety of aspects such as compound screening, efficacy prediction, and clinical (pre) experiment design. However, the use of AI and big data in drug discovery and development also raises corresponding ethical issues, such as data privacy protection and algorithmic transparency. This article will systematically analyze the technological breakthroughs, potential risks, and governance paths of AI and big data in drug development. It will explore how to strengthen the bottom-line of safety and ethics in the Efficiency Revolution and build a responsible innovation ecosystem.
Indexed as
Identifiers
What 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.