Skip to content
2000
image of Revolutionizing Neurodegenerative Disease Management: The Synergy of AI and Pharmacy

Abstract

Neurodegenerative diseases, including Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis (ALS), represent major healthcare challenges worldwide. Despite advances in diagnosis and treatment, these conditions remain incurable, and there is a need for more effective management strategies. The integration of artificial intelligence (AI) in healthcare has emerged as a promising solution, offering new approaches to diagnosis, personalized treatment, and patient care. This paper explores the potential of AI to revolutionize the management of neurodegenerative diseases, with a focus on its synergistic role in pharmacy. By leveraging AI in drug discovery, personalized treatment plans, and clinical decision-making, AI can enhance therapeutic outcomes and improve patient quality of life. The study reviews the current landscape of AI applications in neurodegenerative disease management, with a focus on pharmacy-related interventions. The review includes AI-driven approaches in genomics, biomarkers, drug repurposing, and clinical trials. It also examines AI's role in optimizing pharmaceutical care, improving medication adherence, and tailoring treatments based on individual genetic profiles. AI has demonstrated its capability to analyze vast datasets, from genetic information to clinical records, to identify novel drug targets and predict patient responses to specific therapies. The use of AI in precision medicine has enabled more accurate diagnosis and has facilitated the development of personalized treatments for neurodegenerative diseases. Additionally, AI tools are enhancing medication management by providing personalized therapy adjustments and improving adherence. AI offers transformative potential for the future of neurodegenerative disease management. Its integration into pharmacy practice promises more effective, individualized treatments, accelerating drug discovery, and optimizing patient care. As AI technologies continue to advance, their role in managing complex neurological disorders will become increasingly vital.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cas/10.2174/0118746098375978250820220024
2025-09-11
2025-12-14

  • From This Site

    /content/journals/cas/10.2174/0118746098375978250820220024
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Noorbakhsh F. Overall C.M. Power C. The role of systems biology in neurodegenerative diseases. Biochim. Biophys. Acta 2009 1792 7 654 661 10.1016/j.bbadis.2008.12.001
    [Google Scholar]
  2. Gitler A.D. Dhillon P. Shorter J. Neurodegenerative disease: Models, mechanisms, and a new hope. Dis. Model. Mech. 2017 10 5 499 502 10.1242/dmm.030205 28468935
    [Google Scholar]
  3. Liu Y. Guo Y. Cheng Q. The application of machine learning in predicting clinical outcomes in Alzheimer’s disease. BMC Med. Inform. Decis. Mak. 2021 21 1 36 10.1186/s12911‑021‑01603‑4 33531013
    [Google Scholar]
  4. Noorbakhsh F. Overall C.M. Power C. Deciphering complex mechanisms in neurodegenerative diseases: the advent of systems biology. 2009 Trends in Neurosciences 32 2 88 100
    [Google Scholar]
  5. Shen Y. Li P. Zhang J. AI in precision medicine for neurodegenerative diseases: A systematic review. J. Neural Transm. 2022 129 2 167 180 10.1007/s00702‑021‑02411‑3
    [Google Scholar]
  6. Roy S. Bhan S. Chakraborty M. Genetic biomarkers in Parkinson’s disease: Applications of machine learning models. Front. Neurol. 2021 12 708287 10.3389/fneur.2021.708287
    [Google Scholar]
  7. Gitler A.D. Dhillon P. Shorter J. Neurodegenerative disease: models, mechanisms, and a new hope. 2017 Disease Models & Mechanisms 10 5 499 502 10.1093/bioinformatics/btab765
    [Google Scholar]
  8. Secinaro S. Calandra D. Secinaro A. Muthurangu V. Biancone P. The role of artificial intelligence in healthcare: A structured literature review. BMC Med. Inform. Decis. Mak. 2021 21 1 125 10.1080/14737175.2022.2042951
    [Google Scholar]
  9. Shukla R. Singh R. Das S. AI for disease prognosis and treatment optimization in ALS. 2021 BMC Medical Informatics and Decision Making 21 1 287 10.1186/s12883‑021‑02378‑7 34301198
    [Google Scholar]
  10. Lee C. Jeon Y. Yoon J. AI-based treatment recommendations in Alzheimer’s disease. Alzheimers Dement. 2022 18 6 1067 1075 10.1002/alz.12456
    [Google Scholar]
  11. Cohen J. Ng A. Tomlinson J. AI models in neurodegenerative disease drug screening. Front. Neurosci. 2023 16 110631 10.3389/fnins.2022.110631
    [Google Scholar]
  12. Zeng H. Ma J. Li H. Generative models for drug discovery in neurodegenerative diseases. Neural Comput. Appl. 2023 35 3 1233 1242 10.1007/s00521‑022‑07123‑5
    [Google Scholar]
  13. Verma N. Jain P. Krishnan K. Role of AI in optimizing drug dosage for Parkinson’s disease. Curr. Pharm. Des. 2023 29 2 315 323 10.2174/1381612829666221222112954
    [Google Scholar]
  14. Tsai Y. Kuo C. Huang C. AI-based clinical trial participant selection for neurodegenerative diseases. Transl. Med. 2022 11 1 e428 10.1002/tm.428
    [Google Scholar]
  15. Wang F. Li X. Zhang M. Real-time AI monitoring for clinical trial progress and drug efficacy in neurodegenerative diseases. J. Clin. Med. 2023 12 7 2101 10.3390/jcm12072101
    [Google Scholar]
  16. Zhang W. Zhang Q. Liu D. AI-assisted precision dose adjustment for neurodegenerative disease treatments. J. Drug Target. 2023 31 3 456 462 10.1080/1061186X.2022.2103456 35587560
    [Google Scholar]
  17. Yao Z. Chen P. He L. AI-enhanced medication adherence systems for neurodegenerative diseases. J. Clin. Pharm. Ther. 2022 47 1 101 109 10.1111/jcpt.13556
    [Google Scholar]
  18. Wu X. Dong L. Zhang Y. Artificial intelligence in predicting drug efficacy for neurodegenerative diseases. Pharmacol. Res. 2023 187 106567 10.1016/j.phrs.2022.106567
    [Google Scholar]
  19. Chintala S. AI-Driven Personalised Treatment Plans: The Future of Precision Medicine. Machine Intelligence Research. 2023 17 02 9718 9728 10.1136/medethics‑2021‑107938
    [Google Scholar]
  20. Gull S. Akbar S. Artificial intelligence in brain tumor detection through MRI scans: Advancements and challenges. Artificial Intelligence and Internet of Things 2021 1 36
    [Google Scholar]
  21. Zhang A. Artificial intelligence in neurodegenerative diseases: A review of applications in Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Neuroimage Clin. 2021 30 102594 10.1016/j.nicl.2021.102594
    [Google Scholar]
  22. Patel S. Artificial intelligence in neurodegenerative diseases: Opportunities and challenges. In: Artificial Intelligence in Healthcare. Springer 2024 123 145 10.1007/978‑3‑031‑53148‑4_8
    [Google Scholar]
  23. Barrett C. Altimus C. Artificial intelligence, precision medicine, and neurodegenerative disease: A giving smarter guide. Available from: https://milkeninstitute.org/content-hub/researchand-reports/reports/artificial-intelligence-neurodegenerativedisease-giving-smarter-guide 2024
    [Google Scholar]
  24. Smith J. The future of pharmacy: How AI is revolutionizing the industry. J. Pharm. Sci. 2023 112 3 567 578 10.1016/j.xphs.2022.12.001
    [Google Scholar]
  25. Mangalampalli S. Karri G.R. Shaw A. Exploring AI’s role in managing neurodegenerative disorders: Possibilities and hurdles. In: Studies in Computational Intelligence. Springer 2024 105 131 10.1007/978‑3‑031‑53148‑4_7
    [Google Scholar]
  26. Brown L. Accelerating neurodegenerative clinical drug development with AI. Trends Pharmacol. Sci. 2024 45 2 123 134
    [Google Scholar]
  27. Garcia M. Artificial intelligence in the management of neurological disorders: Current trends and future perspectives. In: Neurological Disorders and Therapeutics. Elsevier 2022 25 47
    [Google Scholar]
  28. Lee K. How artificial intelligence is tackling neurodegenerative diseases. ProClin J 2018 10 4 234 245
    [Google Scholar]
  29. Williams P. AI in neurodegenerative disorders: A neurosurgical perspective. World J Adv Res Rev 2024 14 3 567 580
    [Google Scholar]
  30. Chen R. Demystifying the role of artificial intelligence in neurodegenerative disease management. In: Innovations in Neurology. Springer 2024 1 22
    [Google Scholar]
  31. The convergence of AI technologies and human expertise in pharmaceutical R and D. 2024 Available from: https://www.deloitte.com/uk/en/Industries/life-sciences-health-care/research/the-convergence-of-ai-technologies-and-human-expertise-in-pharma-r-and-d.html
  32. Singh A. Artificial intelligence in the management of neurodegenerative diseases. Curr. Pharm. Des. 2023 29 15 1234 1245
    [Google Scholar]
  33. Bosco C. Otenen E. Osorio Torres J. et al Designing a multimodal and culturally relevant Alzheimer disease and related dementia generative artificial intelligence tool for Black American informal caregivers: Cognitive walk-through usability study. JMIR Aging 2025 8 e60566 10.2196/60566 39778201
    [Google Scholar]
  34. Jump A Elliott L Schneider S et al Examining the relationship between personality factors and neurocognitive functioning in dementia caregivers. Innov Aging 2024 8 4 922(Supp. 1) 10.1093/geroni/igae098.2975
    [Google Scholar]
  35. Patil B. Lad M. Sharma P. Santani N. Rajpal M. Shaikh S. Revolutionizing Alzheimer’s diagnosis using quantum computing. 2024 International Conference on Inventive Computation Technologies (ICICT) Lalitpur, Nepal 24-26 April 2024 479 485 10.1109/ICICT60155.2024.10544797
    [Google Scholar]
  36. Fujii Y.R. Analysis for brain tumors and Alzheimer’s disease using quantum microRNA language with artificial intelligence (MIRAI). SunText Rev Med Clin Res 2021 2 4 142 10.51737/2766‑4813.2021.042
    [Google Scholar]
  37. AI-powered databases boost the Alzheimer's drug discovery process. 2025 vailable from: https://www.wsj.com/articles/ai-powered-databases-boost-the-alzheimers-drug-discovery-process-b9b75180
  38. Xu X. Li J. Zhu Z. et al A comprehensive review on synergy of multi-modal data and AI technologies in medical diagnosis. Bioengineering 2024 11 3 219 10.3390/bioengineering11030219 38534493
    [Google Scholar]
  39. Blanco K. Salcidua S. Orellana P. et al Systematic review: Fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer’s disease. Alzheimers Res. Ther. 2023 15 1 176 10.1186/s13195‑023‑01304‑8 37838690
    [Google Scholar]
  40. Ali H. AI in neurodegenerative disease research: Early detection, cognitive decline prediction, and brain imaging biomarker identification. Int J Eng Technol Res Manag 2022 6 10 71
    [Google Scholar]
  41. Leviss J. HIT or miss: lessons learned from health information technology projects. 3rd ed Boca Raton CRC Press 2019 10.4324/9780429031403
    [Google Scholar]
  42. Sultana A. Rafi A.H. Chowdhury A.A. Tariq M. AI in neurology: Predictive models for early detection of cognitive decline. Rev. Esp. Doc. Cient. 2023 17 2 335 349
    [Google Scholar]
  43. Khanna A. Jones G. Toward personalized medicine approaches for Parkinson disease using digital technologies. JMIR Form. Res. 2023 7 1 e47486 10.2196/47486 37756050
    [Google Scholar]
  44. Reed X. Schumacher-Schuh A. Hu J. Bandres-Ciga S. Advancing personalized medicine in common forms of Parkinson’s disease through genetics: Current therapeutics and the future of individualized management. J. Pers. Med. 2021 11 3 169 10.3390/jpm11030169 33804504
    [Google Scholar]
  45. Dar M.A. Arafah A. Bhat K.A. et al Multiomics technologies: Role in disease biomarker discoveries and therapeutics. Brief. Funct. Genomics 2023 22 2 76 96 10.1093/bfgp/elac017 35809340
    [Google Scholar]
  46. Nancy Noella R.S. Priyadarshini J. Diagnosis of Alzheimer’s and Parkinson’s disease using artificial neural network. Int J Sci Technol Res 2020 9 3 3659 3664
    [Google Scholar]
  47. Chatterjee I. Moradikor N. Integrating neuroimaging, computational neuroscience, and artificial intelligence. Boca Raton CRC Press 2024 10.1201/9781032711102
    [Google Scholar]
  48. Kale M. Wankhede N. Pawar R. et al AI-driven innovations in Alzheimer’s disease: Integrating early diagnosis, personalized treatment, and prognostic modelling. Ageing Res. Rev. 2024 101 102497 10.1016/j.arr.2024.102497 39293530
    [Google Scholar]
  49. Rajaraman S. Artificial intelligence in neuro degenerative diseases: Opportunities and challenges. In: AI and Neuro-Degenerative Diseases. Cham: Springer 2024 1131 133 53 10.1007/978‑3‑031‑53148‑4_8
    [Google Scholar]
  50. Subramanian K. Hajamohideen F. Viswan V. Shaffi N. Mahmud M. Exploring intervention techniques for Alzheimer’s disease: Conventional methods and the role of AI in advancing care. Artif. Intell. Appl. 2024 2 2 59 77
    [Google Scholar]
  51. Khosravi P. Mohammadi S. Zahiri F. Khodarahmi M. Zahiri J. AI‐enhanced detection of clinically relevant structural and functional anomalies in MRI: Traversing the landscape of conventional to explainable approaches. J. Magn. Reson. Imaging 2024 60 6 2272 2289 10.1002/jmri.29247 38243677
    [Google Scholar]
  52. Watts J. Khojandi A. Shylo O. Ramdhani R.A. Machine learning’s application in deep brain stimulation for Parkinson’s disease: A review. Brain Sci. 2020 10 11 809 10.3390/brainsci10110809 33139614
    [Google Scholar]
  53. Oliveira A.M. Coelho L. Carvalho E. Ferreira-Pinto M.J. Vaz R. Aguiar P. Machine learning for adaptive deep brain stimulation in Parkinson’s disease: Closing the loop. J. Neurol. 2023 270 11 5313 5326 10.1007/s00415‑023‑11873‑1 37530789
    [Google Scholar]
  54. Su L. Alattas K.A. Ayadi W. et al A novel fuzzy reinforcement learning approach for personalization deep brain stimulation with communication delay. Biomed. Signal Process. Control 2025 106 107736 10.1016/j.bspc.2025.107736
    [Google Scholar]
  55. Cho C.H. Huang P.J. Chen M.C. Lin C.W. Closed-loop deep brain stimulation with reinforcement learning and neural simulation. IEEE Trans. Neural Syst. Rehabil. Eng. 2024 32 3615 3624 10.1109/TNSRE.2024.3465243 39302783
    [Google Scholar]
  56. Babu B. Parvathy G. Mohideen Bawa F.S. et al Comparing the artificial intelligence detection models to standard diagnostic methods and alternative models in identifying Alzheimer’s disease in at-risk or early symptomatic individuals: A scoping review. Cureus 2024 16 12 e75389 10.7759/cureus.75389 39781179
    [Google Scholar]
  57. Wen J. Nasrallah I.M. Abdulkadir A. et al Genomic loci influence patterns of structural covariance in the human brain. Proc. Natl. Acad. Sci. USA 2023 120 52 e2300842120 10.1073/pnas.2300842120 38127979
    [Google Scholar]
  58. Grollemund V. Pradat P.F. Querin G. et al Machine learning in amyotrophic lateral sclerosis: Achievements, pitfalls, and future directions. Front. Neurosci. 2019 13 135 10.3389/fnins.2019.00135 30872992
    [Google Scholar]
  59. Cheung A.T.M. Nasir-Moin M. Fred Kwon Y.J. et al Methods and impact for using federated learning to collaborate on clinical research. Neurosurgery 2023 92 2 431 438 10.1227/neu.0000000000002198 36399428
    [Google Scholar]
  60. León-Domínguez U. Potential cognitive risks of generative transformer-based AI chatbots on higher order executive functions. Neuropsychology 2024 38 4 293 308 10.1037/neu0000948 38300581
    [Google Scholar]
  61. Dehais F. Ayaz H. Kawala-Sterniuk A. Ladouce S. Barreto C. Karwowski W. Non-Invasive Brain-Computer Interfaces. In: Interaction Techniques and Technologies in Human-Computer Interaction. CRC Press 2025 406 440
    [Google Scholar]
  62. Mann M. Kumar C. Zeng W.F. Strauss M.T. Artificial intelligence for proteomics and biomarker discovery. Cell Syst. 2021 12 8 759 770 10.1016/j.cels.2021.06.006 34411543
    [Google Scholar]
  63. Vrahatis A.G. Skolariki K. Krokidis M.G. Lazaros K. Exarchos T.P. Vlamos P. Revolutionizing the early detection of Alzheimer’s disease through non-invasive biomarkers: The role of artificial intelligence and deep learning. Sensors 2023 23 9 4184 10.3390/s23094184 37177386
    [Google Scholar]
  64. Rajpal S. Rajpal A. Saggar A. et al XAI-MethylMarker: Explainable AI approach for biomarker discovery for breast cancer subtype classification using methylation data. Expert Syst. Appl. 2023 225 120130 10.1016/j.eswa.2023.120130
    [Google Scholar]
  65. Velu K Jaisankar N Design of an early prediction model for Parkinson’s disease using machine learning. IEEE Access 2025 13 17457 72 10.1109/ACCESS.2025.3533703
    [Google Scholar]
  66. Gupta R. Kumari S. Senapati A. Ambasta R.K. Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson’s disease. Ageing Res. Rev. 2023 90 102013 10.1016/j.arr.2023.102013 37429545
    [Google Scholar]
  67. Mubeen H. Masood A. Zafar A. Khan Z.Q. Khan M.Q. Nisa A. Insights into AlphaFold’s breakthrough in neurodegenerative diseases. Ir. J. Med. Sci. 2024 193 5 2577 2588 10.1007/s11845‑024‑03721‑6 38833116
    [Google Scholar]
  68. Vicidomini C. Fontanella F. D’Alessandro T. Roviello G.N. A survey on computational methods in drug discovery for neurodegenerative diseases. Biomolecules 2024 14 10 1330 10.3390/biom14101330 39456263
    [Google Scholar]
  69. Kumar R. Waisberg E. Ong J. et al Artificial intelligence-based methodologies for early diagnostic precision and personalized therapeutic strategies in neuro-ophthalmic and neurodegenerative pathologies. Brain Sci. 2024 14 12 1266 10.3390/brainsci14121266 39766465
    [Google Scholar]
  70. Dhankhar S. Mujwar S. Garg N. et al Artificial intelligence in the management of neurodegenerative disorders. CNS Neurol. Disord. Drug Targets 2024 23 8 931 940 10.2174/0118715273266095231009092603 37861051
    [Google Scholar]
  71. Khachaturian A.S. Dengel A. Dočkal V. Hroboň P. Tolar M. Accelerating innovations for enhanced brain health: Can artificial intelligence advance new pathways for drug discovery for Alzheimer’s and other neurodegenerative disorders? J. Prev. Alzheimers Dis. 2023 10 1 1 4 10.14283/jpad.2023.1 36641602
    [Google Scholar]
  72. Jawad L.A. Security and privacy in digital healthcare systems: Challenges and mitigation strategies. Abhigyan 2024 42 1 23 31
    [Google Scholar]
  73. Bani Issa W. Al Akour I. Ibrahim A. et al Privacy, confidentiality, security and patient safety concerns about electronic health records. Int. Nurs. Rev. 2020 67 2 218 230 10.1111/inr.12585 32314398
    [Google Scholar]
  74. Chevrier R. Foufi V. Gaudet-Blavignac C. Robert A. Lovis C. Use and understanding of anonymization and de-identification in the biomedical literature: Scoping review. J. Med. Internet Res. 2019 21 5 e13484 10.2196/13484 31152528
    [Google Scholar]
  75. Pickering B. Trust, but verify: Informed consent, AI technologies, and public health emergencies. Future Internet 2021 13 5 132 10.3390/fi13050132
    [Google Scholar]
  76. Chinta S.V. Wang Z. Zhang X. Viet T.D. Kashif A. Smith M.A. AI-driven healthcare: A survey on ensuring fairness and mitigating bias.arXiv:2407.19655 2024
/content/journals/cas/10.2174/0118746098375978250820220024
Loading
Revolutionizing Neurodegenerative Disease Management: The Synergy of AI and Pharmacy
Bentham Science Publishers ; https://doi.org/10.2174/0118746098375978250820220024
/content/journals/cas/10.2174/0118746098375978250820220024
/content/journals/cas/10.2174/0118746098375978250820220024
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: pharmacy ; Neurodegenerative diseases ; artificial intelligence ; personalized medicine ; biomarkers ; drug discovery ; precision medicine

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test