Neuro Ai+: Empowering People With Parkinson’s Using An Intelligent System For Personalized Support

jewel islam

doi:10.70771/jocw.247

Vol. 10 No. 1 (2026), Articles

Vol. 10 No. 1 (2026)

Neuro Ai+: Empowering People With Parkinson’s Using An Intelligent System For Personalized Support

Articles

https://doi.org/10.70771/jocw.247

Published 2026-09-15

jewel islam⁺⁻

jewel islam

Journalism and Media Communication Department, Green University of Bangladesh

https://doi.org/10.70771/jocw.247

Download PDF

Add to Mendeley

How to Cite

jewel islam. (2026). Neuro Ai+: Empowering People With Parkinson’s Using An Intelligent System For Personalized Support. Journal Of Creative Writing (ISSN-2410-6259), 10(1), 31–50. https://doi.org/10.70771/jocw.247

Abstract

Neuro AI+ is a novel online healthcare application aimed at aiding people with a diagnosis of Parkinson to live a healthy life with the help of sustainable, intelligent, and personalized care system. The platform uses artificial intelligence, digital health technologies, and user-centred design to overcome the shortcomings of traditional Parkinson care that is often sporadic clinical care and does not support patients on a day-to-day basis. Connected to continuous monitoring, adaptive guidance, and the integration of caregivers, Neuro AI + seeks to enhance patient independence and quality of life as well as long-term disease management.

The project is based on the nexus of healthcare, artificial intelligence, digital health, and assistive neurological technologies. The industry analysis reveals the presence of a good growth environment due to the ageing of the world population, the rise in the prevalence of Parkinson’s disease, and the swift introduction of AI-powered and remote health tools. The distinct industry trends that include customized healthcare, home-based care, and artificial intelligence-based engagement of patients further attest to the commercial and social feasibility of Neuro AI+.

Neuro AI+ will focus its marketing efforts on Parkinson patients as the core market, but caregivers, family members, and healthcare professionals will constitute the significant secondary users. The platform will be user-friendly and convenient, especially to the older demographic, but will also provide useful information and observation resources to caregivers and clinicians. The personalized AI support, constant interaction, patient-centred design, and ethical treatment of sensitive healthcare data are its competitive advantages.

The support of the business model is provided by the well-defined product development and commercialization plan, focusing on the interdisciplinary collaboration, the participatory design, explainable AI, and the regulatory adherence. The development plan will deal with the critical issues of data quality, clinical validation, user confidence, and regulatory compliance using sound validation systems, clinician-in-the-loop solution, and effective intellectual property safeguards.

The marketing and operations plans are aimed at promoting ethically, medical collaboration, subscription-based pricing, and scalable digital distributions. The operations plan provides a safe and cloud-based infrastructure, AI-based personalization, ongoing improvement cycles and a multidisciplinary staffing model to guarantee uninterrupted service delivery and sustainable growth.

Financial plan assumes the initial cost on research, development of AI, compliance with regulations, and infrastructure and then generation of revenue with scales depending on subscription models and institutional partnership. All in all, Neuro AI+ is a socially significant and commercially feasible product addressing a significant void in the management of the Parkinson disease context through the combination of technological innovation and patient-centred care.

Download PDF

References

FinModelsLab. (2024). AI-Powered medical diagnosis business model canvas template. https://finmodelslab.com/products/ai-powered-medical-diagnosis-business-model-canvas

Elena, et al. (2025). PARKA AI: A sensor-integrated mobile application for Parkinson’s disease monitoring and self-management. Bioengineering (open access) Doi 10.3390/bioengineering12101059

Osterwalder, A., & Pigneur, Y. (2010). Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Wiley.

Alrawis, M., Al-Ahmadi, S., & Mohammad, F. (2024). Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data. Applied Sciences 2024, Vol. 14, 14(9). https://doi.org/10.3390/APP14093883

Asil, A. A., Oskouei, A. E., Gilani, N., Khabbazi, M., & Nazari, J. (2025). Designing, prototyping, and evaluating an assistive device for lower limb muscle stretching: With the participatory approach. Human Factors in Healthcare, 8, 100109. https://doi.org/10.1016/J.HFH.2025.100109

Calderone, A., Latella, D., Bonanno, M., Quartarone, A., Mojdehdehbaher, S., Celesti, A., & Calabrò, R. S. (2024). Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders. Biomedicines 2024, Vol. 12, 12(10). https://doi.org/10.3390/BIOMEDICINES12102415

Chandru, M., Abinesh, M., Siva Ananth, M., Vashist, A., & Manickam, P. (2026). Artificial intelligence in neurodegenerative disease diagnosis: Advancing Alzheimer’s and Parkinson’s diseases. Current Opinion in Biomedical Engineering, 37, 100638. https://doi.org/10.1016/J.COBME.2025.100638

Chu, K., Shen, X., & Duan, G. (2026). Precise pre-delivery quality management of dairy products based on consumer feedback driven deep learning and knowledge graph. Microchemical Journal, 117074. https://doi.org/10.1016/J.MICROC.2026.117074

Crombé, A., Bourillon, C., Chaillou, S., Bechet, C., Dupont, J., Bône, A., Louvet-de Verchère, F., Luciani, A., Bellin, M. F., Aubé, C., & Lassau, N. (2025). Understanding the challenges to implementing AI solutions in radiology departments and how to overcome them: A comprehensive review endorsed by the French College of Radiologists (CERF) and the French Society of Radiology (SFR). Diagnostic and Interventional Imaging. https://doi.org/10.1016/J.DIII.2025.12.002

Dharmasivam, M., Kaya, B., Akinware, A., Azad, M. G., & Richardson, D. R. (2026a). Leading artificial intelligence–driven drug discovery platforms: 2025 landscape and global outlook. Pharmacological Reviews, 78(1), 100102. https://doi.org/10.1016/J.PHARMR.2025.100102

Fabbri, M., Rascol, O., Foltynie, T., Carroll, C., Postuma, R. B., Porcher, R., & Corvol, J. C. (2024). Advantages and Challenges of Platform Trials for Disease Modifying Therapies in Parkinson’s Disease. Movement Disorders, 39(9), 1468–1477. https://doi.org/10.1002/MDS.29899

Ferrara, F. (2025). Parkinson’s disease between old drugs and new digital therapies: A pharmacological review. NPG Neurologie - Psychiatrie - Gériatrie, 25(148), 179–186. https://doi.org/10.1016/J.NPG.2025.04.002

Ibba, S., Tancredi, C., Fantesini, A., Cellina, M., Presta, R., Montanari, R., Papa, S., & Alì, M. (2023). How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders. European Journal of Radiology, 165, 110917. https://doi.org/10.1016/J.EJRAD.2023.110917

Li, L., Dai, F., He, S., Yu, H., Liu, H., Li, L., Dai, F., He, S., Yu, H., & Liu, H. (1 C.E.a). Automatic Diagnosis of Parkinson’s Disease Based on Deep Learning Models and Multimodal Data. Https://Services.Igi-Global.Com/Resolvedoi/Resolve.Aspx?Doi=10.4018/979-8-3693-1281-0.Ch009, 179–200. https://doi.org/10.4018/979-8-3693-1281-0.CH009

Malaguti, M. C., Gios, L., & Jurman, G. (2025). The third wheel or the game changer? How AI could team up with neurologists in Parkinson’s care. Parkinsonism & Related Disorders, 134, 107797. https://doi.org/10.1016/J.PARKRELDIS.2025.107797

Moëll, B., & Sand Aronsson, F. (2025). Journaling with large language models: a novel UX paradigm for AI-driven personal health management. Frontiers in Artificial Intelligence, 8, 1567580. https://doi.org/10.3389/FRAI.2025.1567580/BIBTEX

Montaigne, E., Côté, I., Dallaire, C., Durand, P. J., Gagnon, M. P., Giroux, D., Hudon, C., Kröger, E., Légaré, F., Singamalum, S., Sirois, M. J., Tourigny, A., & Giguere, A. (2026a). Exploring the potential of decision aids to support decision-making about healthy aging: A qualitative descriptive study using the theoretical domains framework. Patient Education and Counseling, 142, 109409. https://doi.org/10.1016/J.PEC.2025.109409

Perronne, L., Binvignat, M., Foulquier, N., Saraux, A., Laredo, J. D., de Margerie-Mellon, C., Fournier, L., & Sellam, J. (2025). Algorithmic approaches in hand imaging for rheumatic musculoskeletal diseases: A systematic literature review. Seminars in Arthritis and Rheumatism, 73, 152750. https://doi.org/10.1016/J.SEMARTHRIT.2025.152750

Schulz, P. J., Kee, K. M., Lwin, M. O., Goh, W. W., Chia, K. Y., Cheung, M. F. K., Lam, T. Y. T., & Sung, J. J. Y. (2025). Clinical experience and perception of risk affect the acceptance and trust of using AI in medicine. Frontiers in Digital Health, 7, 1620127. https://doi.org/10.3389/FDGTH.2025.1620127/BIBTEX

Thomas, N. R., Anitha, J., Popirlan, C., Popirlan, C. I., & Hemanth, D. J. (2025). Next-Generation Deep Learning Approaches for Kidney Tumor Image Analysis: Challenges, Clinical Applications, and Future Perspectives. Computers, Materials & Continua, 85(3), 4407–4440. https://doi.org/10.32604/CMC.2025.070689

Úbeda-García, M., Marco-Lajara, B., Zaragoza-Sáez, P. C., & Poveda-Pareja, E. (2025). Artificial intelligence, knowledge and human resource management: A systematic literature review of theoretical tensions and strategic implications. Journal of Innovation & Knowledge, 10(6), 100809. https://doi.org/10.1016/J.JIK.2025.100809

Volpini, L., Prosseda, V., Morpurgo, F., Tsitiridis, A., Krivec, Š. G., Krivec, D., Sajovic, J., & Štrempfel, T. (2025). ETHAI implementation. A Co-Constructive way for Embedding Ethics in Healthcare AI Development. International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2025. https://doi.org/10.1109/ACDSA65407.2025.11166650

Wider, A., Harrer, S., & Dietz, L. W. (2025). AI-Assisted Data Governance with Data Mesh Manager. Proceedings of the IEEE International Conference on Web Services, ICWS, (2025), 963–965. https://doi.org/10.1109/ICWS67624.2025.00126

Willem, T., Fritzsche, M. C., Zimmermann, B. M., Sierawska, A., Breuer, S., Braun, M., Ruess, K., Bak, M., Schönweitz, F. B., Meier, L. J., Fiske, A., Tigard, D., Müller, R., McLennan, S., & Buyx, A. (2024). Embedded Ethics in Practice: A Toolbox for Integrating the Analysis of Ethical and Social Issues into Healthcare AI Research. Science and Engineering Ethics 2024 31:1, 31(1), 3-. https://doi.org/10.1007/S11948-024-00523-Y

Workum, J. D., Meyfroidt, G., Bakker, J., Jung, C., Tobin, J. M., Gommers, D., Elbers, P. W. G., van der Hoeven, J. G., & Van Genderen, M. E. (2026). AI in critical care: A roadmap to the future. Journal of Critical Care, 91, 155262. https://doi.org/10.1016/J.JCRC.2025.155262

Zhao, A., Liu, Y., Yu, X., Xing, X., & Zhou, H. (2025). Artificial intelligence-enabled detection and assessment of Parkinson’s disease using multimodal data: A survey. Information Fusion, 121, 103175. https://doi.org/10.1016/J.INFFUS.2025.103175

Dorsey, E. R., & Bloem, B. R. (2018). The Parkinson pandemic—A call to action. JAMA Neurology, 75(1), 9–10. https://doi.org/10.1001/jamaneurol.2017.3299

Kruse, C. S., Fohn, J., Wilson, N., Nunez Patlan, E., Zipp, S., & Mileski, M. (2019). Utilization barriers and medical outcomes commensurate with the use of telehealth among older adults: Systematic review. JMIR Medical Informatics, 7(4), e14407. https://doi.org/10.2196/14407

Meskó, B., Drobni, Z., Bényei, É. J., Gergely, B., & Győrffy, Z. (2017). Digital health is a cultural transformation of traditional healthcare. mHealth, 3, 38. https://doi.org/10.21037/mhealth.2017.08.07

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

World Health Organization. (2022). Ageing and health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health

Auffret, M., Drapier, S., & Vérin, M. (2021). The many faces of Parkinson's disease: Comprehensive remote monitoring of motor and non-motor symptoms. Frontiers in Neurology, 12, 660142. https://doi.org/10.3389/fneur.2021.660142

Straka, I., Minár, M., Škorvánek, M., Grofik, M., Danterová, K., Benetin, J., & Valkovic, P. (2019). Adherence to pharmacotherapy in patients with Parkinson's disease. Journal of Neural Transmission, 126(9), 1161–1169. https://doi.org/10.1007/s00702-019-02029-7

Bloem, B. R., Okun, M. S., & Klein, C. (2021). Parkinson's disease. The Lancet, 397(10291), 2284–2303. doi:10.1016/S0140-6736(21)00218-X

Espay, A. J., Hausdorff, J. M., Sánchez-Ferro, Á., Klucken, J., Merola, A., Bonato, P., Paul, S. S., Horak, F. B., Vizcarra, J. A., Mestre, T. A., Reilmann, R., Nieuwboer, A., Dorsey, E. R., Rochester, L., Bloem, B. R., & Maetzler, W. (2019). A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies. Movement Disorders, 34(5), 657–663. doi:10.1002/mds.27671

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. doi:10.1177/2053951716679679