Neuropsychologist Vanessa Triviño Burbano explains how artificial intelligence applied to neuroscience is transforming cognitive stimulation and neurorehabilitation, driving early diagnosis, the personalization of therapies and clinical efficiency in addressing cognitive impairment.
Introduction: general context of the relationship between AI and neuroscience
In recent decades, artificial intelligence and neuroscience have redefined the way we think about, diagnose and treat cognitive difficulties. According to Contreras (2023), the human brain possesses complexity and inspires the development of algorithms that emulate processes of attention, memory and learning. According to Jacome et al., (2024) cognitive stimulation and neurorehabilitation are important because they allow clinical interventions to be executed in a personalized way with greater precision and effectiveness.
Di Silvo (2025) mentions that the relationship between AI and neuroscience is fascinating and bidirectional. Neuroscience inspired the design of the first artificial neural networks, which tried to replicate, albeit in a simplified way, how neurons communicate. Currently, those same artificial networks are helping to better understand the processes of the brain and, most importantly, to intervene in them when there are problems.
Zamora et al., (2025) explains that AI has improved the early diagnosis of cognitive impairment, the rehabilitation of patients with acquired brain injury, the support for people with Alzheimer’s and the creation of personalized cognitive stimulation programs. In addition, it can analyze millions of data points in seconds, detect patterns invisible to the human eye and propose intervention strategies adapted to each individual.
For example, patients who previously had few recovery opportunities now have more accessible, dynamic and motivating tools. Families who faced the burden of care right now have digital supports available. And professionals who were limited by lack of time can focus on the most human aspects: accompaniment, compassion and motivation.
The rise of artificial intelligence (AI) in neuroscience and cognitive stimulation
Campolongo (2024) writes that AI in neuroscience is explained by two simultaneous trends: population aging and accelerated technological development.
The World Health Organization (WHO) estimates that by 2050 one in six people in the world will be over 65 years old, and with that the cases of mild cognitive impairment (MCI) and dementias will increase exponentially. Faced with this scenario, traditional cognitive stimulation tools are not sufficient to meet the demand.
By integrating large clinical databases and machine learning algorithms, predictive models can be developed that anticipate the risk of cognitive decline or personalize rehabilitation programs. According to García Cervantes (2025), external neurorrehabilitation strategies enhanced by AI show significant improvements in episodic memory, executive functions and processing speed in older adults.
As Di Salvo (2025) explains, we are experiencing a paradigm shift: from an analog world to a digital one where AI and neuroscience feed back into each other. This intersection not only has scientific value, but also social value, because it allows the design of interventions that respond to the cultural, educational and socioeconomic diversity of each community.
From artificial neural networks to computational neuroscience
Rubio (2022) argues that artificial neural networks were born in the 1950s with Rosenblatt’s perceptron model. Inspired by the basic functioning of neurons, they sought to learn from examples and classify information. Although their capacity was limited at first, today they are the basis of deep learning, which underpins applications as diverse as facial recognition and machine translation.
In neuroscience, these networks have made it possible to model both neurobiological and cognitive processes. This field, called computational neuroscience, not only helps to understand how the mind works, but is also applied to rehabilitation. For example, simulating how the cerebral cortex reorganizes after a stroke makes it possible to design more effective exercises to recover motor and cognitive functions.
How AI is inspired by the human brain
For Lázaro et al., (2024), deep learning algorithms operate on principles that resemble those of the human brain: they make mistakes, correct themselves, reinforce what is useful and discard the irrelevant. Like a child who learns to walk through falls and successes, an AI system adjusts its internal connections until it achieves an accurate result.
This parallel is not only theoretical. In practice, it means that AI can learn to adjust cognitive stimulation programs in real time according to the patient’s response. Thus, if an exercise is too easy, it increases the difficulty; if it generates frustration, it steps back a level. That flexibility, inspired by brain plasticity, is what makes AI such a powerful tool in rehabilitation.
Current applications of AI in neuropsychology and neurorehabilitation
Early detection of cognitive impairment with AI
Early diagnosis is fundamental to intervene in time in cases of MCI or Alzheimer’s. Algorithms capable of analyzing speech, writing or patterns of digital interaction identify micro-signals of decline that humans do not perceive.
These tools, combined with digital cognitive stimulation programs, can improve autonomy and reduce depressive symptoms in people with MCI, especially when integrated into personalized routines (Justo-Henriques et al., 2019).
AI in rehabilitation after a stroke
After a stroke, patients often face long and costly rehabilitation processes. AI, combined with virtual reality, has enabled the creation of exercise programs that automatically adjust to each person’s pace.
In this way, patients use software that proposes motor and cognitive activities that change according to their performance. This not only increases their motivation, but also provides their therapist with objective data to personalize the intervention.
The literature supports these advances: technologies such as mirror therapy, transcranial stimulation and virtual reality, enhanced by AI, have been shown to improve synaptic plasticity and facilitate motor recovery in post-stroke patients (Jácome Vallejo et al., 2024).
AI applications in dementia and Alzheimer’s
In Alzheimer’s and other dementias, AI not only helps in diagnosis but also in daily support. Social robots remind users of basic tasks, promote conversation and reduce loneliness. This complements the work of family members and caregivers, who often face a high emotional burden.
Justo-Henriques et al. (2019) highlight that individualized cognitive stimulation programs supported by digital technologies improve autonomy and quality of life for people with mild neurocognitive disorder.
Personalization of cognitive stimulation programs with AI
Personalization is perhaps AI’s most valuable contribution. Instead of applying the same protocol to everyone, algorithms adjust programs according to each person’s needs, strengths and weaknesses.
A systematic review identified 22 mobile applications with theoretical backing used in cognitive retraining of patients with acquired brain injury (Godoy Fernández, 2024). These tools not only enable adaptive exercises, but also remove economic and geographic barriers, expanding access to therapies.
Benefits of artificial intelligence in clinical and professional practice
The benefits of AI are multiple:
- Early diagnosis: identification of patterns invisible to the human eye.
- Professional efficiency: specialists spend less time on repetitive tasks and more on the human connection.
- Accessibility: quality therapies reach rural or resource-limited areas.
- Motivation: gamified programs that make therapy an engaging experience.
Systematic reviews confirm that AI-supported cognitive stimulation programs remarkably improve memory, executive functions and processing in older adults with MCI (García Cervantes, 2025).
Challenges, risks and ethical limitations of AI in neuroscience and cognitive stimulation
The use of AI in neuroscience is not free of dilemmas. Among the most relevant:
- Privacy: cognitive data are extremely sensitive because they reflect intimate aspects of thought, memory and emotions. Their mishandling could lead to discrimination, manipulation or violation of personal identity.
- Bias: algorithms trained on limited populations can fail in diverse contexts.
- Digital divide: not all patients have access to devices or connectivity.
- Dehumanization: the risk that technology replaces human contact.
The solution is to keep AI as an ally and not a substitute, with solid ethical frameworks that guarantee transparency, equity and human accompaniment.
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The future of artificial intelligence in neuroscience, cognitive stimulation and neurorehabilitation
The future combines science, technology and humanity.
Robotic exoskeletons and adaptive algorithms have already been shown to improve gait and balance in patients with chronic neurological injuries (Jácome Vallejo et al., 2024). The current challenge is to overcome barriers of accessibility and cost, so that these therapies are not a privilege but a right.
Collaboration between neurorehabilitation professionals and artificial intelligence
The true value of AI is achieved in multidisciplinary teams. Engineers, neuropsychologists, occupational therapists and physicians must work together to design culturally sensitive and accessible programs.
As Di Salvo (2025) and García Cervantes (2025) remind us, success depends on combining the best of technology with clinical experience and, above all, with patients’ needs.
Conclusion
Artificial intelligence is effectively transforming cognitive stimulation and neurorehabilitation. Its contributions to early diagnosis, therapy personalization and accessibility represent a paradigm shift. However, its implementation also requires consideration of ethical risks, data privacy and the need to always maintain human supervision.
AI is not an end, but a means for patients to recover autonomy, hope and quality of life. A means for health professionals to devote more time to accompaniment; and for society to move toward a more inclusive, empathetic and ethical future.
If we manage to keep ethics and humanity at the center, the alliance between AI and neuroscience will not only be smarter: it will be more humane.
Bibliography
- Di Salvo, M. (2025). Neuroscience and education in the transition from analog to AI. Revista Internacional de Teoría e Investigación Educativa, 3, e101207. https://doi.org/10.5209/ritie.101207
- García Cervantes, H. T. (2025). Efficacy of external neurorehabilitation strategies in older adults with mild cognitive impairment: a systematic review [Master’s thesis, Universidad de las Américas]. Repositorio Institucional UDLA.
- Godoy Fernández, E. (2024). Review of the use of mobile digital platforms as a cognitive retraining tool in patients with brain injuries. Praxis Psy, 25(41), 1–10. https://doi.org/10.32995/praxispsy.v25i41.270
- Jácome Vallejo, C.A., Mueces Andrango, D.L., & Zambrano Cedeño, G.A. (2024). Neuroplasticity and advanced neurorehabilitation. Journal Growing Health, 1(1), 29–41. https://doi.org/10.59282/jgh1(1)29-41
- Justo-Henriques, S. I., Marques-Castro, A. E., Otero, P., Vázquez, F. L., & Torres, A. J. (2019). Long-term individualized cognitive stimulation program for people with mild neurocognitive disorder: pilot study. Revista de Neurología, 68(7), 281–289. https://doi.org/10.33588/rn.6807.2018321
- Lázaro Guillermo, J. C., Valera Dávila, O., Román Concha, N. U., Guitton Lozano, E., Oliva Paredes, R. J., & Pérez Marín, J. L. (2024). Artificial intelligence for awareness and orientation in educational settings. Editorial Mar Caribe. ISBN 978-9915-9682-8-5. Available at: https://editorialmarcaribe.es/inteligencia-artificial-para-la-conciencia-y-orientacion-en-entornos-educativos/
- Rubio, A. (2022). Artificial neural networks and their contributions to computational neuroscience. Revista de Ciencias Cognitivas, 14(2), 45–60.
- Zamora Mallet, M., Martínez Chile, A., Esteban Garcés, E., & Santos Martínez, Á. M. (2025). Promise of artificial intelligence in the treatment of dementia. GeroInfo-Revista de Gerontología y Geriatría, 20, e317. Sociedad Cubana de Gerontología y Geriatría. https://revgeroinfo.sld.cu/index.php/gerf/article/view/317
Frequently asked questions about AI in cognitive stimulation and neurorehabilitation
1. How is AI transforming neurorehabilitation?
Artificial intelligence (AI) enables the development of personalized cognitive rehabilitation programs, adjusting the difficulty and type of exercises according to the user’s progress. These solutions increase clinical effectiveness and promote data- and neuroplasticity-based neurorehabilitation.
2. What role does AI play in cognitive stimulation?
Adaptive algorithms analyze the user’s response in real time to automatically modify difficulty levels in memory, attention and executive function tasks. This makes AI-driven cognitive stimulation more motivating and effective than traditional programs.
3. How does AI contribute to the early detection of cognitive impairment?
By analyzing patterns of speech, writing and digital interaction, AI can identify micro-signals of mild cognitive impairment (MCI) and help professionals by facilitating early diagnosis and intervention before symptoms become evident.
4. What benefits does artificial intelligence bring to clinical practice in neuroscience?
AI optimizes professional time, improves diagnostic efficiency, expands accessibility to therapies in diverse settings and enhances user motivation through gamified dynamics. It also facilitates evidence-based clinical decisions.
5. What technologies complement AI in cognitive rehabilitation?
Therapies such as transcranial magnetic stimulation, mirror therapy, virtual reality and robotic exoskeletons are combined with AI to design interventions that are more precise and adapted to each user, enhancing motor and cognitive recovery.
6. How is AI used to support people with Alzheimer’s and dementias?
AI supports both early diagnosis and daily cognitive stimulation, through social robots and personalized programs that reinforce memory and reduce loneliness, improving quality of life and caregiver support.
7. What ethical risks does the use of artificial intelligence in neuroscience imply?
Among the main challenges are the privacy of cognitive data, algorithmic biases, the digital divide and the possible dehumanization of therapy. The article emphasizes the importance of keeping AI as an ethical ally, with constant human supervision.
8. Why is collaboration between professionals and AI systems key?
The value of neurorehabilitation with AI lies in multidisciplinary teams that combine engineering, neuropsychology, occupational therapy and medicine. Only by integrating these perspectives are culturally sensitive and accessible programs achieved.
9. What is expected of the future of AI in neuroscience and cognitive stimulation?
The future points to increasingly personalized treatments, with AI capable of integrating sensory, motor and cognitive information. The priority will be to keep ethics and humanity at the center of technological development.
10. How does AI balance technological precision and human empathy?
The text concludes that AI is not an end but a means: a tool in the service of autonomy and hope. Its greatest success lies in freeing clinical time to strengthen the human bond and emotional support.
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