12-04-2025 | Alzheimer's Disease | Review Article

The future of Alzheimer’s disease risk prediction: a systematic review

Author: Sophia Nazir

Abstract

Background

Alzheimer's disease is the most prevalent kind of age-associated dementia among older adults globally. Traditional diagnostic models for predicting Alzheimer's disease risks primarily rely on demographic and clinical data to develop policies and assess probabilities. However, recent advancements in machine learning (ML) and other artificial intelligence (AI) have shown promise in developing personalized risk models. These models use specific patient data from medical imaging and related reports. In this systematic review, different studies comprehensively examined the use of ML in magnetic resonance imaging (MRI), genetics, radiomics, and medical data for Alzheimer's disease risk assessment. I highlighted the results of our rigorous analysis of this research and emphasized the exciting potential of ML methods for Alzheimer's disease risk prediction. We also looked at current research projects and possible uses of AI-driven methods to enhance Alzheimer's disease risk prediction and enable more efficient investigating and individualized risk mitigation strategies.

Aim and methods

This review integrates both conventional and AI-based models to thoroughly analyze neuroimaging and non-neuroimaging features used in Alzheimer's disease prediction. This study examined factors related to imaging, radiomics, genetics, and clinical aspects. In addition, this study comprehensively presented machine learning for predicting the risk of Alzheimer's disease detection to benefit both beginner and expert researchers.

Results

A total of 700 publications from 2000 and 2024, were initially retrieved, out of which 120 studies met the inclusion criteria and were elected for review. The diagnosis of neurological disorders, along with the application of deep learning (DL) and machine learning (ML) were central themes in studies on the subject. When analyzing the medical implementation or design of innovative models, various machine learning models applied to neuroimaging and non-neuroimaging data may help researchers and clinicians become more informed. This review provides an extensive guide to the state of Alzheimer's disease risk assessment with artificial AI.

Conclusion

By integrating diverse neuroimaging and non-neuroimaging data sources, this study provides researchers with an alternative viewpoint on the application of AI in Alzheimer’s disease risk prediction emphasizing its potential to improve early diagnosis and personalized intervention strategies.

Parnetti L, Gaetani L, Eusebi P, Paciotti S, Hansson O, El-Agnaf O, Mollenhauer B, Blennow K, Calabresi P (2019) CSF and blood biomarkers for Parkinson’s disease. Lancet Neurol. https://doi.org/10.1016/S1474-4422(19)30024-9CrossRefPubMed

Gitler AD, Dhillon P, Shorter J (2017) Neurodegenerative disease: models, mechanisms, and a new hope. Dis Model Mech 10(5):499–502CrossRefPubMedPubMedCentral

Li B, Tan H, Jenkins D, Raghavan VS, Rosa BG, Güder F, Pan G, Yeatman E, Sharp DJ (2020) Clinical detection of neurodegenerative blood biomarkers using graphene immunosensor. Carbon. https://doi.org/10.1016/j.carbon.2020.06.048CrossRefPubMedPubMedCentral

Tiwari VAS, Kaushik A, Yndart A, Nair M (2019) Alzheimer’s disease: pathogenesis, diagnostics, and therapeutics. Int J Nanomedicine 14:5541–5554CrossRefPubMedPubMedCentral

Rouhi N, Akhgari A, Orouji N, Nezami A, Rahimzadegan M, Kamal H (2023) Recent progress in the graphene-based biosensing approaches for the detection of Alzheimer’s biomarkers. J Phramaceutical Biomed Anal 222:115084CrossRef

Viola KL, Sbarboro J, Sureka R, De M, Bicca MA, Wang J, Vasavada S, Satpathy S, Wu S, Joshi H, Velasco PT, MacRenaris K, Waters EA, Lu C, Phan J, Lacor P, Prasad P, Dravid VP, Klein WL (2014) Towards non-invasive diagnostic imaging of early-stage Alzheimer’s disease. Nat Nanotechnol 10:91–98. https://doi.org/10.1038/nnano.2014.254CrossRefPubMedPubMedCentral

Parra MA, Orellana P, Leon T, Victoria CG, Henriquez F, Gomez R, Avalos C, Damian A, Slachevsky A, Ibañez A, Zetterberg H, Tijms BM, Yokoyama JS, Piña‐Escudero SD, Cochran JN, Matallana DL, Acosta D, Allegri R, Arias‐Suárez BP, Barra B, Behrens MI, Brucki SMD, Busatto G, Caramelli P, Castro‐Suarez S, Contreras V, Custodio N, Dansilio S, De la Cruz‐Puebla M, de Souza LC, Diaz MM, Duque L, Farías GA, Ferreira ST, Guimet NM, Kmaid A, Lira D, Lopera F, Meza BM, Miotto EC, Nitrini R, Nuñez A, O’Neill S, Ochoa J, Pintado‐Caipa M, de Paula França Resende E, Risacher S, Rojas LA, Sabaj V, Schilling L, Sellek AF, Sosa A, Takada LT, Teixeira AL, Unaucho‐Pilalumbo M, Duran‐Aniotz C (2022) Biomarkers for dementia in Latin American countries: gaps and opportunities. Alzheimer’s Assoc. https://doi.org/10.1002/alz.12757

Padurariu M et al (2012) Hippocampal neuronal loss in the CA1 and CA3 areas of Alzheimer’s disease patients. Psychiatr Danub 24:152–8PubMed

Tanifum EA, Ghaghada K, Vollert C, Head E, Eriksen JL, Annapragada A (2016) A novel liposomal nanoparticle for the imaging of amyloid plaque by magnetic resonance imaging. J Alzheimer’s Dis. https://doi.org/10.3233/JAD-151124CrossRef

10.

Reyes-Santias F et al (2023) Cost analysis of magnetic resonance imaging and computed tomography in cardiology: a case study of a university hospital complex in the euro region. Healthcare 11:2084. https://doi.org/10.3390/healthcare11142084CrossRefPubMedPubMedCentral

11.

Krishnadas N, Villemagne VL, Doré V, Rowe CC (2021) Advances in brain amyloid imaging. Sem Nucl Med 51(3):241–252CrossRef

12.

Patel KP, Wymer DT, Bhatia VK, Duara R, Rajadhyaksha CD (2020) Multimodality imaging of dementia: clinical importance and role of integrated anatomic and molecular imaging. Radiographics 40(1):200–22. https://doi.org/10.1148/rg.2020190070CrossRefPubMed

13.

Ding Y et al (2019) A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology 290(2):456–464CrossRefPubMed

14.

Etminani K et al (2022) A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer’s disease, and mild cognitive impairment using brain 18F-FDG PET. Eur J Nucl Med Mol Imaging 49:1–22CrossRef

15.

Raju VPGM, Anitha VS, Wahid KA (2020) Multi-class diagnosis of Alzheimer’s disease using cascaded three dimensional-convolutional neural network. Phys Eng Sci Med 43(2):1219–1228CrossRefPubMed

16.

Salvatore C et al (2014) Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy. J Neurosci Methods 222:230–237CrossRefPubMed

17.

Reddy JFS, Purohit MP (2019) Artificial intelligence-enabled healthcare delivery. J R Soc Med 112(1):22–28CrossRefPubMed

18.

Soffer AB-CS, Shimon O, Amitai MM, Greenspan H, Klang E (2019) Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology 290(3):590–606CrossRefPubMed

19.

Zhang W, Li Y, Ren W, Liu B (2023) Artificial intelligence technology in Alzheimer’s disease research. Intractable Rare Dis Res 12(4):208–212. https://doi.org/10.5582/irdr.2023.01091CrossRefPubMedPubMedCentral

20.

Fabrizio C, Termine A, Caltagirone C, Sancesario G (2021) Artifical intrelligence for Alzheimer’s disease: promise or challenge? Diagnostics 11(8):1473. https://doi.org/10.3390/diagnostics11081473CrossRefPubMedPubMedCentral

21.

Fathi S, Ahmadi M, Dehand A (2022) Early diagnois of Alzheimer’s diseae based on deep learning: a systematytic review. Comptures Biol Med 146:105634. https://doi.org/10.1016/j.compbiomed.2022.105634CrossRef

22.

Arya AD et al (2023) A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease. Brain Inform 10(1):17. https://doi.org/10.1186/s40708-023-00195-7CrossRefPubMedPubMedCentral

23.

Singh KR, Dash S (2023) Early detection of neurological diseases using machine learning and deep learning techniques: a review. artificial intelligence for neurological disorders. Artif Intell Neurol Disord 1–24. https://doi.org/10.1016/B978-0-323-90277-9.00001-8

24.

Norfray JF, Provenzale JM (2004) Alzheimer’s disease: neuropathologic findings and recent advances in imaging. Am J Roentgenol 182(1):3–13. https://doi.org/10.2214/ajr.182.1.1820003CrossRef

25.

Wahlund LO, Julin P, Johansson SE, Scheltens P (2000) Visual rating and volumetry of the medial temporal lobe on magnetic resonance imaging in dementia: a comparative study. J Neurol Neurosurg Psychiatry 69(5):630–635. https://doi.org/10.1136/jnnp.69.5.630CrossRefPubMedPubMedCentral

26.

Miller-Thomas MM, Sipe AL, Benzinger TLS, McConathy J, Connolly S, Schwetye KE (2016) Multimodality review of amyloid-related diseases of the central nervous system. Radiographics 36(4):1147–63. https://doi.org/10.1148/rg.2016150172CrossRefPubMed

27.

Electrochemical immunosensor based on superwettable microdroplet array for detecting multiple Alzheimer’s disease biomarkers. https://doi.org/10.3389/fbioe.2022.1029428

28.

Sheng J, Xin Y, Zhang Q, Wang L, Yang Z, Yin J (2022) Predictive classification of Alzheimer’s disease using brain imaging and genetic data. Sci Rep 12. https://doi.org/10.1038/s41598-022-06444-9

29.

Gupta Y, Lama RK, Kwon G-R (2019) Prediction and classification of Alzheimer’s Disease based on combined features from apolipoprotein-e genotype, cerebrospinal fluid, MR, and FDG-PET imaging biomarkers. Front Comput Neurosci 13. https://doi.org/10.3389/fncom.2019.00072

30.

Miller-Thomas MM, Sipe AL, Benzinger TLS, McConathy J, Connolly S, Schwetye KE (2016) Multimodality review of amyloid-related diseases of the central nervous system. Radiographics 36(4):1147–1163. https://doi.org/10.1148/rg.2016150172CrossRefPubMed

31.

Oldan JD, Jewells VL, Pieper B, Wong TZ (2021) Complete evaluation of dementia: PET and MRI correlation and diagnosis for the neuroradiologist. AJNR Am J Neuroradiol 42(6):998–1007. https://doi.org/10.3174/ajnr.A7079CrossRefPubMedPubMedCentral

32.

Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim T-H (2017) An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI. Comput Methods Programs Biomed 140:307–320. https://doi.org/10.1016/j.cmpb.2017.01.003CrossRefPubMed

33.

Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim T-H (2017) An iterative implementation of level set for precise segmentation of brain tissues and abnormality detection from MR images. IETE J Res 63(6):769–783. https://doi.org/10.1080/03772063.2017.1331757CrossRef

34.

Jang I, Li B, Riphagen JM, Dickerson BC, Salat DH (2022) Multiscale structural mapping of Alzheimer’s disease neurodegeneration. NeuroImage: Clin 33:102948. https://doi.org/10.1016/j.nicl.2022.102948CrossRefPubMed

35.

Hett K, Ta V-T, Oguz I, Manjón JV, Coupé P (2021) Multi-scale graph-based grading for Alzheimer’s disease prediction. Med Image Anal 67:101850CrossRefPubMed

36.

Tomek A, Urbanová B, Hort J (2014) Utility of transcranial ultrasound in predicting Alzheimer’s disease risk. J Alzheimers Dis 42:S365–S374. https://doi.org/10.3233/jad-141803CrossRefPubMed

37.

Tomek A, Urbanova B, Hort J (2014) Utility of transcranial ultrasound in predicting Alzheimer’s disease risk. J Alzheimer’s Dis 42:S365–S374CrossRef

38.

Leinenga G et al (2024) Scanning ultrasound-mediated memory and functional improvements do not require amyloid-β reduction. Mol Psychiatry. https://doi.org/10.1038/s41380-024-02509-5CrossRefPubMedPubMedCentral

39.

Leinenga G, Götz J (2015) Scanning ultrasound removes amyloid-beta and restores memory in an Alzheimer’s disease mouse model. Sci Transl Med 7(278): https://doi.org/10.1126/scitranslmed.aaa2512

40.

Andrews SJ, Renton AE, Fulton-Howard B, Podlesny-Drabiniok A, Marcora E, Goate AM (2023) The complex genetic architecture of Alzheimer's disease: novel insights and future directions. eBioMedicine 90. https://doi.org/10.1016/j.ebiom.2023.104511

41.

Wang S, Fang X, Wen X, Yang C, Yang Y, Zhang T (2023) Prioritization of risk genes for Alzheimer’s disease: an analysis framework using spatial and temporal gene expression data in the human brain based on support vector machine. Front Genet 14. https://doi.org/10.3389/fgene.2023.1190863

42.

Cogill S, Wang L (2016) Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates. Bioinformatics 32(23):3611–3618. https://doi.org/10.1093/bioinformatics/btw498CrossRefPubMed

43.

Hill MA, Gammie SC (2022) Alzheimer’s disease large-scale gene expression portrait identifies exercise as the top theoretical treatment. Sci Rep 12. https://doi.org/10.1038/s41598-022-22179-z

44.

Fernandez-Alvarez M, Atienza M, Cantero JL (2022) Effects of non-modifiable risk factors of Alzheimer’s disease on intracortical myelin content. Alzheimers Res Ther 14. https://doi.org/10.1186/s13195-022-01152-y

45.

Fratiglioni L, Ahlbom A, Viitanen M, Winblad B (1993) Risk factors for late-onset Alzheimer’s disease: a population-based, case-control study. Ann Neurol 33:258–266. https://doi.org/10.1002/ana.410330306CrossRefPubMed

46.

Tsai MS, Tangalos EG, Petersen RC, Smith GE, Schaid DJ, Kokmen E et al (1994) Apolipoprotein E: risk factor for Alzheimer disease. Am J Hum Genet 54(4):643PubMedPubMedCentral

47.

Van Duijn CM, Clayton D, Chandra V, Fratiglioni L, Graves AB, Heyman A et al (1991) Familial aggregation of Alzheimer’s disease and related disorders: a collaborative re-analysis of case-control studies. Int J Epidemiol 20:S13–S20. https://doi.org/10.1093/ije/20.Supplement_2.S13CrossRefPubMed

48.

Matthews KA, Xu W, Gaglioti AH et al (2019) Racial and ethnic estimates of Alzheimer’s disease and related dementias in the United States (2015–2060) in adults aged ≥65 years. Alzheimers Dement 15:17–24CrossRefPubMed

49.

Sindi S et al (2015) The CAIDE dementia risk score app: the development of an evidence-based mobile application to predict the risk of dementia. Alzheimers Dement (Amst) 1(3):328–333CrossRefPubMed

50.

Hall A et al (2024) The Australian National University Alzheimer's Disease Risk Index (ANU-ADRI) score as a predictor for cognitive decline and potential surrogate outcome in the FINGER lifestyle randomized controlled trial. Randomized Control Trial 31(5): https://doi.org/10.1111/ene.16238

51.

Röhr S, Pabst A, Baber R et al (2022) Social determinants and lifestyle factors for brain health: implications for risk reduction of cognitive decline and dementia. Sci Rep 12. https://doi.org/10.1038/s41598-022-16771-6.

52.

You J, Zhang Y-R, Wang H-F, Yang M, Feng J-F, Yu J-T, Cheng W (2022) Development of a novel dementia risk prediction model in the general population: a large, longitudinal, population-based machine-learning study. eClinicalMedicine. https://doi.org/10.1016/j.eclinm.2022.101665CrossRefPubMedPubMedCentral

53.

Geethadevi GM et al (2022) Validity of three risk prediction models for dementia or cognitive impairment in Australia. Age Ageing 51(12). https://doi.org/10.1093/ageing/afac307

54.

Moscoso A et al (2019) Prediction of Alzheimer’s disease dementia with MRI beyond the short-term: implications for the design of predictive models. Neuroimage Clin 23:101837. https://doi.org/10.1016/j.nicl.2019.101837CrossRefPubMedPubMedCentral

55.

Mofrad SA, Lundervold AJ, Vik A, Lundervold AS (2021) Cognitive and MRI trajectories for prediction of Alzheimer’s disease. Sci Rep 11. https://doi.org/10.1038/s41598-020-78095-7

56.

Yao Z et al (2023) Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images. Eur J Radiol 165. https://doi.org/10.1016/j.ejrad.2023.110934

57.

Menagadevi M, Devaraj S, Madian N, Thiyagarajan D (2024) Machine and deep learning approaches for alzheimer disease detection using magnetic resonance images: an updated review. Measurement 226. https://doi.org/10.1016/j.measurement.2023.114100

58.

Gautam R, Sharma M (2020) Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. J Med Syst 49. https://doi.org/10.1007/s10916-019-1519-7

59.

Álvarez I, Górriz JM, Ramírez J, Salas-Gonzalez D, López M, Puntonet CG, Segovia F (2009) Alzheimer’s diagnosis using Eigenbrains and support vector machines. Electron Lett 5517:973-980. https://doi.org/10.1049/el.2009.3415

60.

Dakdareh SG, Abbasian K (2024) Diagnosis of Alzheimer’s disease and mild cognitive impairment using convolutional neural networks. J Alzheimers Dis Rep 8(1):317–328. https://doi.org/10.3233/ADR-230118CrossRefPubMedPubMedCentral

61.

Olaimat MA, Martinez J, Saeed F, Bozdag S, Alzheimer’s Disease Neuroimaging Initiative (2023) PPAD: a deep learning architecture to predict progression of Alzheimer’s disease. BioRix 2:239. https://doi.org/10.1101/2023.01.28.526045CrossRef

62.

Bae J, Stocks J, Heywood A, Jung Y, Jenkins L, Hill V, Katsaggelos A, Popuri K, Rosen H, Beg MF et al (2020) Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer’s type based on a three-dimensional convolutional neural network. Neurobiol Aging 99:53–64. https://doi.org/10.1016/j.neurobiolaging.2020.12.005CrossRefPubMedPubMedCentral

63.

Liu J, Li M, Luo Y, Yang S, Li W, Bi Y (2021) Alzheimer’s disease detection using depthwise separable convolutional neural networks. Comput Methods Programs Biomed 203:106032. https://doi.org/10.1016/j.cmpb.2021.106032CrossRefPubMed

64.

Samhan LF, Alfarra AH, Abu-Naser SS (2022) Classification of Alzheimer’s disease using convolutional neural networks. Int J Acad Inf Syst Res (IJAISR) 6(3):18–23

65.

Yang K, Mohammed EA, Far BH (2021) Detection of Alzheimer's disease using graph-regularized convolutional neural network based on structural similarity learning of brain magnetic resonance images. 2021 IEEE 22nd international conference on information reuse and integration for data science (IRI), pp. 326–333. https://doi.org/10.48550/arXiv.2102.13517

66.

Cui Z, Leng J, Zhang T, Quan P, Zhao W (2019) Alzheimer’s disease diagnosis using enhanced inception network based on brain magnetic resonance image. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2324–2330. https://doi.org/10.1109/BIBM47256.2019.8983046

67.

Patil V, Madgi M, Kiran A (2022) Early prediction of Alzheimer's disease using convolutional neural network: a review. Egypt J Neurol Psychiatry Neurosurg 58(1). https://doi.org/10.1186/s41983-022-00571-w

68.

Yang K, Far BH (2020) A review of artificial intelligence technologies for early prediction of Alzheimer’s disease. arXiv. https://doi.org/10.48550/arXiv.2101.01781

69.

Venugopalan J, Tong L, Hassanzadeh HR et al (2021) Multimodal deep learning models for early detection of Alzheimer’s disease stage. Sci Rep 11. https://doi.org/10.1038/s41598-020-74399-w

70.

Pradhan A, Gige J, Eliazer M (2021) Detection of Alzheimer’s disease (AD) in MRI images using deep learning. Int. J. Eng. Res. Technol. 2021 Apr;10(3):580-5.

71.

Khatun M, Islam MM, Rifat HR, Shahid MSB, Talukder MA, Uddin MA (2023) Hybridized convolutional neural networks and long short-term memory for improved alzheimer’s disease diagnosis from MRI scans. In: 2023 26th International Conference on Computer and Information Technology (ICCIT). IEEE, pp 1-6

72.

Diogo VS, Ferreira HA, Prata D (2022) Early diagnosis of Alzheimer’s disease using machine learning: a multi-diagnostic, generalizable approach. Alzheim Res Ther 14. https://doi.org/10.1186/s13195-022-01047-y

73.

Li H, Habes M, Wolk DA, Fan Y (2020) A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal MRI. Alzheimers Dement 8. https://doi.org/10.1016/j.jalz.2019.02.007

74.

De Gregorio G, Desiato D, Marcelli A, Polese G (2021) A multi classifier approach for supporting Alzheimer’s diagnosis based on handwriting analysis. Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part I. https://doi.org/10.1007/978-3-030-68763-2_43

75.

Impedovo D, Pirlo G, Vessio G et al (2019) A handwriting-based protocol for assessing neurodegenerative dementia. Cogn Comput 11. https://doi.org/10.1007/s12559-019-09642-2

76.

Bahado-Singh RO et al (2021) Artificial intelligence and leukocyte epigenomics: evaluation and prediction of late-onset Alzheimer’s disease. PLoS One. https://doi.org/10.1371/journal.pone.0248375CrossRefPubMedPubMedCentral

77.

Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Dage JL, Stomrud E, Janelidze S, Mattsson-Carlgren N, Hansson O (2021) Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med 27:1034–1042. https://doi.org/10.1038/s41591-021-01348-zCrossRefPubMed

78.

Xue C, Kowshik SS, Lteif D et al (2024) AI-based differential diagnosis of dementia etiologies on multimodal data. Nat Med. https://doi.org/10.1038/s41591-024-03118-zCrossRefPubMedPubMedCentral

79.

Jahan S et al (2023) Explainable AI-based Alzheimer’s prediction and management using multimodal data. PLoS One. https://doi.org/10.1371/journal.pone.0294253CrossRefPubMedPubMedCentral

80.

Golovanevsky M, Eickhoff C, Singh R (2022) Multimodal attention-based deep learning for Alzheimer’s disease diagnosis. J Am Med Inform Assoc. https://doi.org/10.1093/jamia/ocac168CrossRefPubMedPubMedCentral

81.

Oatman SR, Reddy JS, Quicksall Z, Carrasquillo MM, Wang X, Liu C-C, Yamazaki Y, Nguyen TT, Malphrus K, Heckman M, Biswas K, Nho K, Baker M, Martens YA, Zhao N, Kim JP, Risacher SL, Rademakers R, Saykin AJ, DeTure M, Murray ME, Kanekiyo T; Alzheimer’s Disease Neuroimaging Initiative; Dickson DW, Bu G, Allen M, Ertekin-Taner N (2023) Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer’s disease related proteins. Mol Neurodegener 18(2). https://doi.org/10.1186/s13024-022-00592-2

82.

Spasov S, Passamonti L, Duggento A, Liò P, Toschi N (2019) A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. NeuroImage 189. https://doi.org/10.1016/j.neuroimage.2019.01.031

83.

Franzmeier N, Koutsouleris N, Benzinger T, Goate A, Karch CM, Fagan AM, McDade E, Duering M, Dichgans M, Levin J et al (2020) Predicting Sporadic Alzheimer’s Disease Progression via Inherited Alzheimer’s Disease-Informed Machine-Learning. Alzheimer’s Dement 16:501–511. https://doi.org/10.1002/alz.12032CrossRef

84.

85.

Appel J, Potter E, Shen Q, Pantol G, Greig MT, Loewenstein D, Duara R (2009) A comparative analysis of structural brain MRI in the diagnosis of Alzheimer’s disease. Behav Neurol 21:13–19CrossRefPubMedPubMedCentral

86.

Fabrizio C, Termine A, Caltagirone C, Sancesario G (2021) Artificial intelligence for Alzheimer’s disease: promise or challenge? Diagnostics 11:1473. https://doi.org/10.3390/diagnostics11081473CrossRefPubMedPubMedCentral

87.

Rogers J, Luber-Narod J, Styren SD, Civin WH (1988) Expression of immune system-associated antigens by cells of the human central nervous system: Relationship to the pathology of Alzheimer’s disease. Neurobiol Aging 9:339–349CrossRefPubMed

88.

Mennella C, Maniscalco U, De Pietro G, Esposito M (2024) Ethical and regulatory challenges of AI technologies in healthcare: a narrative review. Heliyon 10:e26297. https://doi.org/10.1016/j.heliyon.2024.e26297CrossRefPubMedPubMedCentral

89.

Naik N, Hameed BMZ, Shetty DK, Swain D, Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K, Shetty S, Rai BP, Chlosta P, Somani BK (2022) Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Front Surg 9. https://doi.org/10.3389/fsurg.2022.862322.

90.

Williamson SM, Prybutok V (2024) Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Appl Sci 14:675. https://doi.org/10.3390/app14020675CrossRef

91.

Solanki P, Grundy J, Hussain W (2023) Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers. AI Ethics 3:223–240. https://doi.org/10.1007/s43681-022-00195-zCrossRef

92.

Bekbolatova M, Mayer J, Ong CW, Toma M (2024) Transformative potential of ai in healthcare: definitions, applications, and navigating the ethical landscape and public perspectives. Healthcare 12. https://doi.org/10.3390/healthcare12020125

93.

Karalis VD (2024) The integration of artificial intelligence into clinical practice. Appl Biosci 3. https://doi.org/10.3390/applbiosci3010002

Title: The future of Alzheimer’s disease risk prediction: a systematic review
Author: Sophia Nazir
Publication date: 12-04-2025
Publisher: Springer International Publishing
Keywords: Alzheimer's Disease
Alzheimer Disease
Artificial Intelligence
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Dementia
Dementia
Published in: Neurological Sciences
Print ISSN: 1590-1874
Electronic ISSN: 1590-3478
DOI: https://doi.org/10.1007/s10072-025-08167-x

How can you integrate PET into your practice? (Link opens in a new window)

Alzheimer Disease
Independent Medical Education

PET imaging is playing an increasingly critical role in managing AD. Our expert-led program will empower you with practical strategies and real-world case studies to effectively integrate it into clinical practice.

This content is intended for healthcare professionals outside of the UK.

Supported by:

Lilly

Developed by: Springer Healthcare IME

Keynote series | Spotlight on menopause

Menopause
Video Series

Menopause can have a significant impact on the body, with effects ranging beyond the endocrine and reproductive systems. Learn about the systemic effects of menopause, so you can help patients in your clinics through the transition.

Prof. Martha Hickey

Dr. Claudia Barth

Dr. Samar El Khoudary

Developed by: Springer Medicine

Watch now