ArticleNeurology international2024
Artificial Intelligence as a Replacement for Animal Experiments in Neurology: Potential, Progress, and Challenges.
Article in Neurology international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers, 1 of them a synthesis that pooled it.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the potential of computer simulation models in drug testing and biomedical research: a systematic review.Frontiers in pharmacology · 2025Pooled it
- Correction: Rudroff, T. Artificial Intelligence as a Replacement for Animal Experiments in Neurology: Potential, Progress, and Challenges.Neurology international · 2026Article
- Botanical and Upcycled Bioactives for Advanced Topical Formulations: Mechanistic Pathways, Cutaneous Delivery, and Sustainability-by-Design.Pharmaceutics · 2026Review
- Evaluation of neurotoxicity of drugs from morphological and electrophysiological endpoints using human iPSC-derived neural cell models.Frontiers in cellular and infection microbiology · 2026Article
- Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.Frontiers in pharmacology · 2026Review
- Bridging biology and technology: the rise of 3D bioprinting advancements in infection research.Frontiers in bioengineering and biotechnology · 2026Review
- Decoding the Bioactivity of Prunus armeniaca L. Essential Oil: Cheminformatics-Supported Exploration of Its Antioxidant and Antibacterial Potential.Chemistry & biodiversity · 2026Article
- Strength and perception of computational neuroscience among Nigerian students and early career researchers.IBRO neuroscience reports · 2025Article
- Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- The 3C (Cell Culture, Computer Simulation, Clinical Trial) Solution for Optimizing the 3R (Replace, Reduction, Refine) Framework during Preclinical Research Involving Laboratory Animals.ACS pharmacology & translational science · 2025Review
- Artificial intelligence-, organoid-, and organ-on-chip-powered models to improve pre-clinical animal testing of vaccines and immunotherapeutics: potential, progress, and challenges.Frontiers in artificial intelligence · 2025Review
- The impact of artificial intelligence on drug discovery for neuropsychiatric disorders.EXCLI journal · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Animal experimentation has long been a cornerstone of neurology research, but it faces growing scientific, ethical, and economic challenges. Advances in artificial intelligence (AI) are providing new opportunities to replace animal testing with more human-relevant and efficient methods. This article explores the potential of AI technologies such as brain organoids, computational models, and machine learning to revolutionize neurology research and reduce reliance on animal models. These approaches can better recapitulate human brain physiology, predict drug responses, and uncover novel insights into neurological disorders. They also offer faster, cheaper, and more ethical alternatives to animal experiments. Case studies demonstrate AI's ability to accelerate drug discovery for Alzheimer's, predict neurotoxicity, personalize treatments for Parkinson's, and restore movement in paralysis. While challenges remain in validating and integrating these technologies, the scientific, economic, practical, and moral advantages are driving a paradigm shift towards AI-based, animal-free research in neurology. With continued investment and collaboration across sectors, AI promises to accelerate the development of safer and more effective therapies for neurological conditions while significantly reducing animal use. The path forward requires the ongoing development and validation of these technologies, but a future in which they largely replace animal experiments in neurology appears increasingly likely. This transition heralds a new era of more humane, human-relevant, and innovative brain research.
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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.