At a neonatal ward in Kathmandu, a doctor studies retinal images from a premature baby. To most people, the images look ordinary. To that doctor, they carry the weight of a lifetime. If early signs of abnormal brain and blood vessel development are missed, the child may grow up with permanent vision loss, learning difficulties, or both. In Nepal, where trained specialists are few and unevenly distributed, such decisions are often made under intense pressure, with limited support and little room for error. This is exactly where artificial intelligence should no longer be treated as a futuristic luxury, but as a public health necessity.
Artificial intelligence is already reshaping how neuroscience is practiced around the world. The real question for Nepal is not whether AI belongs in brain and neurological care, but whether we are willing to adopt it thoughtfully or allow preventable disability to continue simply because systems have not evolved.
At its core, neuroscience is about understanding how the brain develops, adapts, and sometimes fails. Artificial intelligence, on the other hand, is built to recognize patterns in vast and complex information. When these two fields come together, AI does not replace doctors or neuroscientists. Instead, it acts as a powerful assistant, helping humans see patterns that are difficult to detect consistently, especially when time, expertise, or resources are limited. For a country like Nepal, this partnership is not optional. It is strategic, practical, and necessary.
The evidence for this is no longer theoretical. A study published in Ophthalmology Science evaluated a deep learning system used to screen premature infants in Nepal for retinopathy of prematurity. The system performed with near-perfect accuracy, achieving an area-under-the-curve value of 0.999, using retinal imaging devices already available in Nepali hospitals. This was not an experiment in a high-income country with ideal conditions. It was tested in real hospitals, with real patients, and real constraints. The researchers concluded that AI could dramatically expand screening capacity, reduce pressure on scarce specialists, and enable earlier interventions, where delays often cost children in their futures.
This matters because retinopathy of prematurity is not just an eye disease. It reflects disrupted development of the brain’s blood vessels during a critical window of early life. Preventing severe disease is not only about saving vision; it is about protecting long-term neurological development. When artificial intelligence can reliably identify subtle warning signs earlier than the human eye, choosing not to use it becomes more than a missed opportunity. It raises serious ethical concerns.
The stakes extend far beyond neonatal care. Nepal is undergoing a demographic and epidemiological transition. As deaths from infectious diseases decline and life expectancy increases, neurological and mental health conditions are becoming more common. Conditions such as stroke, dementia, epilepsy, depression, and Parkinson’s disease now account for a growing share of disability. Data from the Global Burden of Disease study make this trend clear. Yet neurologists, psychiatrists, and advanced diagnostic facilities remain concentrated in a few urban centers. Expecting this system to meet future demand without technological support is simply unrealistic.
Public health researchers writing in the Nepal Journal of Epidemiology have pointed out that artificial intelligence could help improve diagnosis, predict risk, and guide population-level planning. But they also offer important warnings. If Nepal relies entirely on imported algorithms trained on foreign populations, it risks reinforcing inequity rather than reducing it. Health data reflect genetics, language, culture, and environment. AI tools must be validated locally, governed ethically, and paired with investment in Nepali expertise, not treated as black boxes delivered from abroad.
Encouragingly, Nepali scholars themselves have emphasized this balance. A 2025 article in the Journal of Universal College of Medical Sciences compared artificial intelligence and human brain function from a physiological perspective. Their conclusion was refreshingly grounded. AI is faster and more precise when handling large amounts of data. Humans remain superior in judgment, ethics, emotional understanding, and contextual decision-making. In healthcare, the goal is not competition, but collaboration. Machines should manage repetitive and data-heavy tasks so clinicians can focus on care, compassion, and responsibility.
Still, enthusiasm without caution is dangerous. Generative AI tools are now entering medical education and research, including in Nepal. A 2024 review in the Journal of Institute of Medicine Nepal highlighted both their promise and their risks. Issues such as data privacy, security, and confidently incorrect outputs are real concerns, particularly when dealing with sensitive brain and health information. These tools are powerful, but without training and oversight, they can mislead just as easily as they can assist. This is why education matters as much as technology. Studies on AI adoption in Nepal show that while awareness is increasing, access and digital literacy remain uneven, especially outside major cities. If clinicians are expected to rely on AI tools without understanding their strengths and limitations, the result will be mistrust or misuse.
Nepal now stands at a crossroads. Artificial intelligence in neuroscience is no longer a distant idea discussed only in conferences and journals. It is already helping detect disease earlier, analyze complex brain data, and support clinical decisions in resource-limited settings. The real danger lies not in adopting AI, but in doing so passively, without local data, ethical safeguards, and human oversight. The path forward is clear. Nepal must invest in digital health infrastructure, encourage collaboration between engineers, clinicians, and neuroscientists, and develop national guidelines that place ethics and equity at the center of AI use. Artificial intelligence should be treated as a public good, not a private experiment or a marketing slogan.
Used wisely, AI can help a general doctor in a district hospital recognize a neurological emergency before it is too late. It can help a premature child avoid a lifetime of preventable disability. Choosing not to act is itself a decision, one that disproportionately harms those with the least access to care. The future of neuroscience in Nepal will not be written by machines alone. It will be shaped by whether we choose to use these tools responsibly, locally, and humanely. The technology is ready. The evidence is strong. What remains is the collective will to act.
The author is a PhD candidate in the Department of Neurosciences and Neurological Disorders at the University of Toledo
