Professional Perspectives: Innovation Without Cruelty: Replacing Animals with Artificial Intelligence
By Anjali Kumar | April 23rd, 2026
Millions of animals are still used in research every year, even as new technologies continue to reshape what is possible in science. Among these developments, artificial intelligence is beginning to expand the boundaries of scientific understanding, offering approaches that may gradually reduce the need for traditional animal models. Artificial intelligence, one of the most powerful emerging technologies of our time, is increasingly being used to replace animals in research and testing, particularly in the field of neurology.
Artificial intelligence (AI) is defined as an entity that observes its environment and makes decisions to optimize its likelihood of successfully completing a given task, objective, or outcome. Traditional computing has a hard time adapting to varied situations and has shown great promise in testing and research. However, AI can assess potential actions and adjust to unexpected situations. This capability, often referred to as "utility" in academic literature, is what sets artificial intelligence apart. Currently, AI-based models are already being used in research and testing for several neurological disorders such as Alzheimer's, Parkinson's disease, and Epilepsy. These machine learning models are helping to "identify new drug targets and predict the neurotoxicity of compounds," reducing reliance on animal models. AI further advances these methods by enabling the development of "patient-specific models to optimize surgical interventions" for conditions like Epilepsy.
The use of animals in neurological research and testing presents significant limitations, such as predicting human brain function and the progression of neurological diseases. For example, numerous neuroprotective drugs that have shown promise in animal models for treating acute stroke ultimately fail in human clinical trials. As a result, the development of these drugs is often abandoned, and research facilities may cease further investigation. One study suggests that a key reason for these failures is that preclinical neuroprotection studies tend to focus on preserving cerebral white matter. However, the rat brain, which is commonly used in such research, contains a much smaller proportion of white matter compared to the human brain. Using animals with different biological structures from humans can limit the applicability of the results to human cases. Artificial intelligence may be an effective replacement for animals in neurology. Another study used machine learning to analyze brain imaging data and identify potential biomarkers for Alzheimer's disease in weeks, a process that previously took years of animal studies. These are some of the ways AI-based models have provided benefits. Although computer modeling has long been used as a substitute, the adoption of AI further enhances its capabilities.
However, there are regulatory and ethical considerations and challenges for artificial intelligence in research. There would have to be a nationwide joint effort to fully validate AI-based technology. "Regulators, industry, academia, and other stakeholders would have to establish best practices and standards" for AI developers and researchers to follow. Another consideration is that "the use of AI in neurology research can raise ethical questions around transparency, bias, and accountability." AI is also a complex and difficult technology, which can create issues in decision-making. Moreover, privacy concerns are increasing nationwide as technology advances. Sensitive patient data and information would have to be released to researchers and the AI technology model itself to execute properly. There would have to be careful governance established over such data, or litigation and liability could arise. Moreover, another weakness to take into consideration is human judgment. AI cannot mimic human judgment; it has an advanced computational mind. AI should not entirely replace human researchers. It is critical to receive the researcher's judgment through decision-making to avoid incorrect policy or clinical decisions.
To that end, AI has enabled scientific progress and reduced the need for animal models. It provides new insights for researchers, showing that it can replace animal experiments and enable more efficient research. There are challenges and limitations to consider before implementing AI to replace animal models; however, the potential of this new technology will vastly change how we view research and testing worldwide.
Anjali Kumar is a 3rd year law student at West Virginia University College of Law and a dedicated animal law advocate focused on modernizing animal protection through policy and legislative work. She has worked in legislative advocacy, judicial research, and animal law organizations, with experience advancing reforms on animal testing, shelter policy, state and local code, and emerging legal tools that challenge outdated systems and drive meaningful change.
The views expressed do not necessarily reflect the official policy or position of Johns Hopkins University or Johns Hopkins Bloomberg School of Public Health.
