Abstract
Medical applications of Artificial Intelligence (AI) have consistently shown remarkable performance in providing medical professionals and patients with support for complex tasks. Nevertheless, the use of these applications in sensitive clinical domains where high-stakes decisions are involved could be much more extensive if patients, medical professionals, and regulators were provided with mechanisms for trusting the results provided by AI systems. A key issue for achieving this is endowing AI systems with key dimensions of Trustworthy AI (TAI), such as fairness, transparency, robustness, or accountability, which are not usually considered within this context in a generalized and systematic manner. This paper reviews the recent advances in the TAI domain, including TAI standards and guidelines. We propose several requirements to be addressed in the design, development, and deployment of TAI systems and present a novel machine learning pipeline that contains TAI requirements as embedded components. Moreover, as an example of how current AI systems in medicine consider the TAI perspective, the study extensively reviews the recent literature (2017–2021) on AI systems in a prevalent and high social-impact disease: diagnosis and progression detection of Alzheimer’s Disease (AD). The most relevant AI systems in the AD domain are compared and discussed (such as machine learning, deep learning, ensembles, time series, and multimodal multitask) from the perspective of how they address TAI in their design. Several open challenges are highlighted, which could be claimed as one of the main reasons to justify the rare application of AI systems in real clinical environments. The study provides a roadmap to measure the TAI status of an AI systems and highlights its limitations. In addition, it provides the main guidelines to overcome these limitations and build medically trusted AI-based applications in the medical domain.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
References
Abadi M et al (2016) Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp 308–318
Abrol A, Bhattarai M, Fedorov A, Du Y, Plis S (2020) Calhoun V (2020) Deep residual learning for neuroimaging: an application to predict progression to Alzheimer’s disease. J Neurosci Methods 339:108701
Abuhmed T, El-sappagh S, Alonso JM (2021) Robust hybrid deep learning models for Alzheimer’s progression detection. Knowledge-Based Syst 213:106688
Adeli E et al (2019) Semi-supervised discriminative classification robust to sample-outliers and feature-noises. IEEE Trans Pattern Anal Mach Intell 41(2):515–522
Adler P et al (2018) Auditing black-box models for indirect influence. Knowl Inf Syst 54(1):95–122
Ahmed R et al (2019a) Neuroimaging and machine learning for dementia diagnosis : recent advancements and future prospects. IEEE Rev Biomed Eng 12:19–33
Ahmed S et al (2019b) Ensembles of patch-based classifiers for diagnosis of alzheimer diseases. IEEE Access 7:73373–73383
AI HLEG (2019) Policy and investment recommendations for trustworthy AI. Brussels Indep High-Level Expert Gr Artif Intell (AI HLEG), Rep Publ by Eur. Commun, p 52
Alberdi A, Aztiria A, Basarab A (2016) On the early diagnosis of Alzheimer’s disease from multimodal signals: a survey. Artif Intell Med 71:1–29
Ali S, El-Sappagh S, Ali F, Imran M, Abuhmed T (2022) Multitask deep learning for cost-effective prediction of patient’s length of stay and readmission state using multimodal physical activity sensory data. IEEE J Biomed Heal Inform, 1–14
Alonso J, Castiello C, Magdalena L, Mencar C (2021) Explainable fuzzy systems: paving the way from interpretable fuzzy systems to explainable AI systems, 1st edn. Springer, Berlin Heidelberg
Alonso JM, Bugar A (2019) ExpliClas : automatic generation of explanations in natural language for weka classifiers. In: IEEE international conference on fuzzy systems, pp 1–6
Al-Rubaie M, Chang JM (2019) Privacy-preserving machine learning: threats and solutions. IEEE Secur Priv 17(2):49–58
Altaf T, Anwar SM, Gul N, Majeed MN, Majid M (2018) Multi-class Alzheimer’s disease classification using image and clinical features. Biomed Signal Process Control 43:64–74
An N, Ding H, Yang J, Au R, Ang TFA (2020) Deep ensemble learning for Alzheimer’s disease classification. J Biomed Inform 105:103411
Arachchige PCM, Bertok P, Khalil I, Liu D, Camtepe S, Atiquzzaman M (2020) A trustworthy privacy preserving framework for machine learning in industrial IoT systems. IEEE Trans Ind Inform 16(9):6092–6102
Armañanzas R, Iglesias M, Morales DA, Alonso-Nanclares L (2017) Voxel-based diagnosis of alzheimer’s disease using classifier ensembles. IEEE J Biomed Heal Informatics 21(3):778–784
Arnold M et al (2018) FactSheets: Increasing trust in ai services through supplier’s declarations of conformity. IBM J Res Dev 63(4):1–13
Arras L, Montavon G, Müller K-R, Samek W (2017) Explaining recurrent neural network predictions in sentiment analysis. arXiv1706.07206
Arrieta AB et al (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI”. Inf Fusion 58:82–115
Arya V et al (2020) Ai explainability 360: an extensible toolkit for understanding data and machine learning models. J Mach Learn Res 21:1–6
Ashmore R, Calinescu R, Paterson C (2019) Assuring the machine learning lifecycle: desiderata, methods, and challenges. arXiv
Association A (2019) Alzheimer’s disease factsand figures. Alzheimer’s Dement 15(3):321–387
Auret L, Aldrich C (2012) Interpretation of nonlinear relationships between process variables by use of random forests. Miner Eng 35:27–42
Babapour-Mofrad R et al (2019) Decision tree supports the interpretation of CSF biomarkers in Alzheimer’s disease. Alzheimer’s Dement Diagn Assess Dis Monit 11:1–9
Bach S, Binder A, Montavon G, Klauschen F, Müller K-R, Samek W (2015) On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7):e0130140
Bai Z, Watson F, Yu DR, Shi H, Yuan Y, Zhang Y (2009) Abnormal resting-state functional connectivity of posterior cingulate cortex in amnestic type mild cognitive impairment. Brain Res 1302:167–174
Baker RS, Hawn A (2021) Algorithmic bias in education. Springer, New York
Baniecki H, Kretowicz W, Piatyszek P, Wisniewski J, Biecek P (2020) Dalex: responsible machine learning with interactive explainability and fairness in python. arXiv, pp 1–7
Barakat NH, Bradley AP (2007) Rule extraction from support vector machines: a sequential covering approach. IEEE Trans Knowl Data Eng 19(6):729–741
Barreno M, Nelson B, Joseph AD, Tygar JD (2010) The security of machine learning. Mach Learn 81(2):121–148
Basaia S et al (2019) Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin. 21:101645
Basheera S, Sai-Ram MS (2019) Convolution neural network–based Alzheimer’s disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation. Alzheimer’s Dement Transl Res Clin Interv 5:974–986
Basheera S, Satya-Sai-Ram M (2020) A novel CNN based Alzheimer’s disease classification using hybrid enhanced ICA segmented gray matter of MRI. Comput Med Imaging Graph 81:101713
Baskar D, Jayanthi VS, Jayanthi AN (2019) An efficient classification approach for detection of Alzheimer’s disease from biomedical imaging modalities. Multimed Tools Appl 78(10):12883–12915
Bass C et al (2021) ICAM-reg: interpretable classification and regression with feature attribution for mapping neurological phenotypes in individual scans. IEEE Trans Med Imaging 20:1–13
Bastani O, Ioannou Y, Lampropoulos L, Vytiniotis D, Nori A, Criminisi A (2016) Measuring neural net robustness with constraints. arXiv1605.07262
Bateman RJ et al (2012) Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 367(9):795–804
Bayram E, Caldwell JZK, Banks SJ (2018) Current understanding of magnetic resonance imaging biomarkers and memory in Alzheimer ’ s disease. Alzheimer’s Dement Transl Res Clin Interv 4:395–413
Beheshti I, Demirel H, Matsuda H (2017) Classification of Alzheimer’s disease and prediction of mild cognitive impairment-to-Alzheimer’s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med 83(February):109–119
Bellamy RKE et al (2019) AI fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. IBM J Res Dev 63(4/5):1–1
Ben-Braiek H, Khomh F (2020) On testing machine learning programs. J Syst Softw 164:110542
Bender EM, Friedman B (2018) Data statements for natural language processing: toward mitigating system bias and enabling better science. Trans Assoc Comput Linguist 6(May):587–604
Benton A, Mitchell M, Hovy D (2017) Multi-task learning for mental health using social media text. arXiv
Berk R, Heidari H, Jabbari S, Kearns M, Roth A (2018) Fairness in criminal justice risk assessments: The state of the art. Sociol Methods Res, 0049124118782533
Beutel A, Chen J, Zhao Z, Chi EH (2017) Data decisions and theoretical implications when adversarially learning fair representations. arXiv
Beutel A, et al (2019) Putting fairness principles into practice: challenges, metrics, and improvements. In: Proceedings of the 2019 AAAI/ACM conference on AI, Ethics, and Society, pp 453–459
Bi XA, Hu X, Wu H, Wang Y (2020) Multimodal data analysis of alzheimer’s disease based on clustering evolutionary random forest. IEEE J Biomed Heal Informatics 24(10):2973–2983
Biffi C et al (2020) Explainable anatomical shape analysis through deep hierarchical generative models. IEEE Trans Med Imaging 39(6):2088–2099
Biggio B, Roli F (2018) Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognit 84:317–331
Biggio B, Fumera G, Roli F (2013) Security evaluation of pattern classifiers under attack. IEEE Trans Knowl Data Eng 26(4):984–996
Bin Bae J, et al (2020) Identification of Alzheimer’s disease using a convolutional neural network model based on T1—weighted magnetic resonance imaging. Sci Rep, pp 1–10
Binth M, Noor T, Zenia NZ, Kaiser MS, Al Mamun S, Mahmud M (2020) Application of deep learning in detecting neurological disorders from magnetic resonance images : a survey on the detection of Alzheimer ’ s disease, Parkinson ’ s disease and schizophrenia. Brain Inform
Birkenbihl C et al (2020) Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia—lessons for translation into clinical practice. EPMA J 11(3):367–376
Blackman R (2020) A practical guide to building ethical AI. Harvard Bus Rev, vol 15
Blanco-Justicia A, Domingo-Ferrer J, Martínez S, Sánchez D (2020) Machine learning explainability via microaggregation and shallow decision trees. Knowledge-Based Syst 194:105532
Blennow H, Zetterberg K (2018) Biomarkers for Alzheimer’s disease: current status and prospects for the future. J Intern Med 284:643–663
Borg M, et al (2021) Exploring the assessment list for trustworthy ai in the context of advanced driver-assistance systems. In: Proc.—2021 IEEE/ACM 2nd int. work. ethics softw. eng. res. pract. ethics 2021, pp 5–12
Brand L, Nichols K, Wang H, Shen L, Huang H (2020) Joint multi-modal longitudinal regression and classification for alzheimer’s disease prediction. IEEE Trans Med Imaging 39(6):1845–1855
Brati B, Zoran O (2018) Machine learning for predicting cognitive diseases : methods , data sources and risk factors
Bron EE et al (2021) Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease. NeuroImage Clin 31:102712
Bruun M et al (2019) Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study. Alzheimer’s Res Ther 11(1):1–11
Bucholc M et al (2019) A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual. Expert Syst Appl 130:157–171
Budd S, Robinson EC, Kainz B (2019) Survey on active learning and human-in-the-loop deep learning for medical image analysis. arXiv 71, 102062
Buruk B, Ekmekci PE, Arda B (2020) A critical perspective on guidelines for responsible and trustworthy artificial intelligence. Med Heal Care Philos 23(3):387–399
Buvaneswari PR, Gayathri R (2021) Deep learning-based segmentation in classification of alzheimer’s disease. Arab J Sci Eng 46(6):5373–5383
Calders T, Verwer S (2010) Three naive Bayes approaches for discrimination-free classification. Data Min Knowl Discov 21(2):277–292
Calmon FP, Wei D, Vinzamuri B, Ramamurthy KN, Varshney KR (2017) Optimized pre-processing for discrimination prevention. In Proceedings of the 31st international conference on neural information processing systems, pp 3995–4004
Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp), pp 39–57
Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N (2015) Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1721–1730
Carvalho CM, Seixas FL, Conci A, Muchaluat-Saade DC, Laks J, Boechat Y, A dynamic decision model for diagnosis of dementia, Alzheimer’s disease and Mild Cognitive Impairment. Comput Biol Med 126
Caton S, Haas C (2020) Fairness in machine learning: a survey. arXiv, pp 1–33
Cearns M, Hahn T, Baune BT (2019) Recommendations and future directions for supervised machine learning in psychiatry. Transl Psychiatry. https://doi.org/10.1038/s41398-019-0607-2
Celik B, Vanschoren J (2021) Adaptation strategies for automated machine learning on evolving data. IEEE Trans Pattern Anal Mach Intell
Celis LE, Huang L, Keswani V, Vishnoi NK (2019) Classification with fairness constraints: a meta-algorithm with provable guarantees. In: Proceedings of the conference on fairness, accountability, and transparency, pp 319–328
Chang H, Shokri R (2020) On the privacy risks of algorithmic fairness. arXiv
Chen Y, Xia Y (2021) Iterative sparse and deep learning for accurate diagnosis of Alzheimer’s disease. Pattern Recognit 116:107944
Cheng L, Varshney KR, Liu H (2021) Socially responsible AI algorithms: issues, purposes, and challenges. J Artif Int Res 71:1137–1181
Cherepanova V, Nanda V, Goldblum M, Dickerson JP, Goldstein T (2021) Technical challenges for training fair neural networks. arXiv2102.06764
Chetelat G (2018) Multimodal neuroimaging in alzheimer’s disease : early diagnosis, physiopathological mechanisms, and impact of lifestyle. J Alzheimer’s Dis 64:S199–S211
Chiappa S (2019) Path-specific counterfactual fairness. Proc AAAI Conf Artif Intell 33(01):7801–7808
Chitradevi D, Prabha S (2020) Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease. Appl Soft Comput J 86:105857
Choi E, Bahadori MT, Kulas JA, Schuetz A, Stewart WF, Sun J (2016) RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism. Adv Neural Inf Process Syst 3512–3520
Choi JY, Lee B (2020) Combining of multiple deep networks via ensemble generalization loss, based on MRI images, for alzheimer’s disease classification. IEEE Signal Process Lett 27:206–210
Chouldechova A (2017) Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2):153–163
Chouldechova A, G’Sell M (2017) Fairer and more accurate, but for whom?.arXiv1707.00046
Corbeil ME, Corbeil JR (2021) Establishing trust in artificial intelligence in education. In: Trust, organizations and the digital economy. Routledge, pp 49–60
Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining, pp 797–806.
Cortez P, Embrechts MJ (2013) Using sensitivity analysis and visualization techniques to open black box data mining models. Inf Sci (ny) 225:1–17
Cotter A et al (2019) Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals. J Mach Learn Res 20(172):1–59
Cowgill B, Tucker C (2017) Algorithmic bias: a counterfactual perspective. NSF Trust. Algorithms
Crockett KA, Gerber L, Latham A, Colyer E (2021) Building trustworthy AI solutions: a case for practical solutions for small businesses. IEEE Trans Artif Intell
Crone SF, Lessmann S, Stahlbock R (2006) The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing. Eur J Oper Res 173(3):781–800
Cruz RMO, Sabourin R, Cavalcanti GDC (2018) Dynamic classifier selection: recent advances and perspectives. Inf Fusion 41:195–216
Cruz AF, Saleiro P, Belém C, Soares C, Bizarro P Promoting fairness through hyperparameter optimization, vol 1, no 1. Association for Computing Machinery.
Cui R, Liu M (2019) RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Comput Med Imaging Graph 73:1–10
Cutillo CM et al (2020) “Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency. Npj Digit Med 3(1):1–5
Das D, Ito J, Kadowaki T, Tsuda K (2019) An interpretable machine learning model for diagnosis of Alzheimer’s disease. PeerJ 7:e6543
Dasgupta P, Collins J (2019) A survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks. AI Mag 40(2):31–43
De Bruyn A, Viswanathan V, Beh YS, Brock JKU, von Wangenheim F (2020) Artificial intelligence and marketing: pitfalls and opportunities. J Interact Mark 51:91–105
Deng H (2019) Interpreting tree ensembles with inTrees. Int J Data Sci Anal 7(4):277–287
Di Stefano PG, Hickey JM, Vasileiou V (2020) Counterfactual fairness: removing direct effects through regularization. arXiv2002.10774
Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10(7):1895–1923
Dignum V (2019) Responsible artificial intelligence: how to develop and use AI in a responsible way. Springer Nature, NewYork
Dimitriadis SI, Liparas D (2018) Random forest feature selection, fusion and ensemble strategy: combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer’s disease patients: from the alzheimer’s disease neuroimaging initiative (ADNI) data. J Neurosci Methods 302:14–23
Ding X et al (2018) A hybrid computational approach for efficient Alzheimer’s disease classification based on heterogeneous data. Sci Rep 8(1):1–10
Diprose WK, Buist N, Hua N, Thurier Q, Shand G, Robinson R (2020) Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator. J Am Med Inform Assoc 27(4):592–600
Divya R, Shantha Selva Kumari R (2021) Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification. Neural Comput Appl, vol 0123456789
Dua M, Makhija D, Manasa PYL, Mishra P (2020) A CNN–RNN–LSTM based amalgamation for Alzheimer’s disease detection. J Med Biol Eng 40(5):688–706
Duc NT, Ryu S, Qureshi MNI, Choi M, Lee KH, Lee B (2020) 3D-deep learning based automatic diagnosis of alzheimer’s disease with joint MMSE prediction using resting-state fMRI. Neuroinformatics 18(1):71–86
Duchesne S, Caroli A, Geroldi C, Collins DL, Frisoni GB (2009) Relating one-year cognitive change in mild cognitive impairment to baseline MRI features. Neuroimage 47(4):1363–1370
Dunn C, Moustafa N, Turnbull B (2020) Robustness evaluations of sustainable machine learning models against data poisoning attacks in the internet of things. Sustainability 12(16):6434
Durán JM, Jongsma KR (2021) Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J Med Ethics. https://doi.org/10.1136/medethics-2020-106820
Durongbhan P et al (2019) A dementia classification framework using frequency and time-frequency features based on EEG signals. IEEE Trans Neural Syst Rehabil Eng 27(5):826–835
Dwork C, Immorlica N, Kalai AT, Leiserson M (2018) Decoupled classifiers for group-fair and efficient machine learning. In: Conference on fairness, accountability and transparency, pp 119–133
Dyrba M et al (2020) Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease. arXiv
Ebrahimighahnavieh A, Luo S, Chiong R (2020) Deep learning to detect Alzheimer ’ s disease from neuroimaging: a systematic literature review. Comput Methods Programs Biomed 187:105242
Eke CS, Jammeh E, Li X, Carroll C, Pearson S, Ifeachor E (2021) early detection of Alzheimer’s disease with blood plasma proteins using support vector machines. IEEE J Biomed Heal Inform 25(1):218–226
El Sappagh S, Alonso JM, Islam SMR, Sultan AM (2021) A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease. Sci Rep. https://doi.org/10.1038/s41598-021-82098-3
El-Gamal FEZA et al (2020) Personalized computer-aided diagnosis for mild cognitive impairment in alzheimer’s disease based on sMRI and C PiB-PET analysis. IEEE Access 8:218982–218996
El-Rashidy N et al (2022) Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning. Neural Comput Appl 34(5):3603–3632
El-Sappagh S, Elmogy M, Riad AM (2015) A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis. Artif Intell Med 65(3):179–208
El-Sappagh S, Alonso JM, Ali F, Ali A, Jang JH, Kwak KS (2018) An ontology-based interpretable fuzzy decision support system for diabetes diagnosis. IEEE Access 6:37371–37394
El-Sappagh T, Abuhmed SM, Riazul-Islam KS (2020) Kwak, “Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data.” Neurocomputing 412:197–215
El-Sappagh S, Ali F, Abuhmed T, Singh J, Alonso JM (2022a) Automatic detection of Alzheimer’s disease progression: an efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing 512:203–224
El-Sappagh S, Saleh H, Ali F, Amer E, Abuhmed T (2022b) Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time. Neural Comput Appl 34:14487–14509
El-Sappagh S et al (2021) Alzheimer’s disease progression detection model based on an early fusion of cost-effective multimodal data. Futur Gener Comput Syst 115
Emaminejad N, Akhavian R (2022) Trustworthy AI and robotics: Implications for the AEC industry. Autom Constr 139:104298
Er F, Goularas D (2021) Predicting the prognosis of MCI patients using longitudinal MRI data. IEEE/ACM Trans Comput Biol Bioinform 18(3):1164–1173
Casanova R, Barnard RT, Gaussoin SA, Saldana S, Hayden KM, Manson JE, Wallace RB, Rapp SR, Resnick SM, Espeland MA, Chen J-C, et al (2018) Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer’s disease across imaging databases. Neuroimage 183:401–411
Hochrangige Expertengruppe für Künstliche Intelligenz (HEG-KI), High-Level Expert Group on Artificial Intelligence (2020) Assessment list for trustworthy AI (ALTAI) for self assessment
Falahati F, Institutet K, Westman E, Institutet K, Simmons A (2014) Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. J Alzheimer’s Dis 41:685–708
Fang C et al (2020) Gaussian discriminative component analysis for early detection of Alzheimer’s disease: A supervised dimensionality reduction algorithm. J Neurosci Methods, 344(July):108856
Feldman M, Friedler SA, Moeller J, Scheidegger C, Venkatasubramanian S (2015) Certifying and removing disparate impact. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 259–268
Feng R, Yang Y, Lyu Y, Tan C, Sun Y, Wang C (2019) Learning fair representations via an adversarial framework. arXiv1904.13341
Fenu G, Galici R, Marras M (2022) Experts’ view on challenges and needs for fairness in arti cial intelligence for education. arXiv:2207.01490v1
Fiddler (2022) Build ethical AI using explainable AI
Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam L, Kohane IS (2019) Adversarial attacks on medical machine learning. Science 363(6433):1287–1290
Finlayson SG, Kohane IS, Beam AL (2018) Beam adversarial attacks against medical deep learning systems. arXiv1804.05296
Fisher CK, Smith AM, Walsh JR (2018) Deep learning for comprehensive forecasting of Alzheimer’s disease progression. arXiv1807.03876
Forouzannezhad P et al (2019) A survey on applications and analysis methods of functional magnetic resonance imaging for Alzheimer’s disease. J Neurosci Methods 317:121–140
Forouzannezhad P et al (2020) A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging. J Neurosci Methods 333:108544
Franke CGK (2012) Longitudinal changes in individual Brain AGE in healthy aging, mild cognitive impairment, and Alzheimer’s disease. GeroPsych (bern) 25(4):235–245
Fukunishi H, Nishiyama M, Luo Y, Kubo M, Kobayashi Y (2020) Alzheimer-type dementia prediction by sparse logistic regression using claim data. Comput Methods Programs Biomed 196:105582
Fung BCM, Wang K, Chen R, Yu PS (2010) Privacy-preserving data publishing: a survey of recent developments. ACM Comput Surv 42(4):1–53
Gacto MJ, Alcalá R, Herrera F (2011) Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures. Inf Sci (ny) 181(20):4340–4360
Gade K, Geyik SC, Kenthapadi K, Mithal V, Taly A (2019) Explainable AI in industry. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3203–3204
Gao J, Wang B, Lin Z, Xu W, Qi Y (2017) Deepcloak: masking deep neural network models for robustness against adversarial samples. arXiv1702.06763
Gardiner J, Nagaraja S (2016) On the security of machine learning in malware c&c detection: a survey. ACM Comput Surv 49(3):1–39
Gardner J, Brooks C (2017) Statistical approaches to the model comparison task in learning analytics.MLA/BLAC@ LAK
Gebru T et al (2021) Datasheets for datasets. Commun ACM 64(12):86–92
Georges N, Mhiri I, Rekik I (2020) Identifying the best data-driven feature selection method for boosting reproducibility in classification tasks. Pattern Recognit 101:107183
Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G (2018) Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 178(11):1544–1547
Gillen S, Jung C, Kearns M, Roth A Online learning with an unknown fairness metric. arXiv Prepr. arXiv1802.06936 (2018)
Gillespie N, Curtis C, Bianchi R, Akbari A, van Vlissingen R (2020) Achieving trustworthy AI: a model for trustworthy artificial intelligence
Goel N, Amayuelas A, Deshpande A, Sharma A (2020) The importance of modeling data missingness in algorithmic fairness: a causal perspective. arXiv2012.11448
Goh G, Cotter A, Gupta M, Friedlander M (2016) Satisfying real-world goals with dataset constraints. Adv neural inf. process. syst., Nips 2016, pp 2423–2431
Gómez-Sancho M, Tohka J, Gómez-Verdejo V (2018) Comparison of feature representations in MRI-based MCI-to-AD conversion prediction. Magn Reson Imaging 50(March):84–95
Gonzalez Zelaya CV (2019) Towards explaining the effects of data preprocessing on machine learning. In: Proc.—int. conf. data eng., vol. 2019-April, pp 2086–2090
González-Gonzalo C et al. (2021) Trustworthy AI: closing the gap between development and integration of AI systems in ophthalmic practice. Prog Retin Eye Res
Goodfellow I, et al (2014) Generative adversarial nets. Adv Neural Inf Process Syst
Gopinath D, Pasareanu CS, Wang K, Zhang M, Khurshid S (2019) Symbolic execution for attribution and attack synthesis in neural networks. In: Proc—2019 IEEE/ACM 41st int. conf. softw. eng. companion, ICSE-companion 2019, pp 282–283
Graham SA et al (2020) Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: a conceptual review. Psychiatry Res 284:112732
Grgic-Hlaca N, Zafar MB, Gummadi KP, Weller A (2016) The case for process fairness in learning: feature selection for fair decision making. NIPS Symp Mach Learn Law 1:1
Gu S, Rigazio L (2015) Towards deep neural network architectures robust to adversarial examples. In: Workshop paper at international conference on learning representative (ICLR)
Guerrero R, Schmidt-Richberg A, Ledig C, Tong T, Wolz R, Rueckert D et al (2016) Instantiated mixed effects modeling of Alzheimer’s disease markers. Neuroimage 142:113–125
Guidotti R, Monreale A, Ruggieri S, Turini F, Pedreschi D, Giannotti F (2018) A survey of methods for explaining black box models. ACM Comput Surv 51(5):931–9342
Gyori G, Bachman BM, Subramanian JA, Muhlich K, et al (2017) “From word models to executable models of signaling networks using automated assembly. Mol Syst Biol, 13:954
Haas C (2019) The price of fairness-a framework to explore trade-offs in algorithmic fairness. In: 40th international conference on information systems, ICIS
Hajian S, Bonchi F, Castillo C (2016) Algorithmic bias: From discrimination discovery to fairness-aware data mining. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 2125–2126
Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. arXiv1610.02413
Hatherley JJ (2020) Limits of trust in medical AI. J Med Ethics 46(7):478–481
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in medicine. Nat Med 25(1):30–36
He X, Zhao K, Chu X (2021) AutoML: a survey of the state-of-the-art. Knowledge-Based Syst 212:106622
He Y, Meng G, Chen K, Hu X, He J (2020) Towards security threats of deep learning systems: a survey. IEEE Trans Softw Eng
Hedayati R, Khedmati M, Taghipour-Gorjikolaie M (2021) Deep feature extraction method based on ensemble of convolutional auto encoders: application to Alzheimer’s disease diagnosis. Biomed Signal Process Control 66:102397
Holmes W, et al. (2021) Ethics of AI in education: towards a community-wide framework. Int J Artif Intell Educ, 504–526
Hong X et al (2019) Predicting alzheimer’s disease using LSTM. IEEE Access 7:80893–80901
Hossain MA, Ferdousi R, Alhamid MF (2020) Knowledge-driven machine learning based framework for early-stage disease risk prediction in edge environment. J Parallel Distrib Comput 146:25–34
Huang M et al (2017) Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease. Sci Rep 7:1–13
Huang X et al (2020) A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability. Comput Sci Rev 37:100270
Hutiri WT, Ding AY (2022) Towards trustworthy edge intelligence : insights from voice-activated services. In: IEEE serv. comput. conf., 2022.
Ignatiev A, Cooper MC, Siala M, Hebrard E, Marques-Silva J (2020) Towards formal fairness in machine learning. In: Lect. notes comput. sci. (including subser. lect. notes artif. intell. lect. notes bioinformatics), vol 12333 LNCS, pp 846–867
Iosifidis V, Fetahu B, Ntoutsi E (2019) Fae: a fairness-aware ensemble framework. In 2019 IEEE international conference on big data (big data), pp 1375–1380
Ito K et al (2011) Disease progression model for cognitive deterioration from Alzheimer’s disease neuroimaging initiative database. Alzheimer’s Dement 7(2):151–160
Jacobs M et al. (2021) Designing AI for trust and collaboration in time-constrained medical decisions: a sociotechnical lens. In: Proc. 2021 CHI conf. hum. factors comput. syst., pp 1–14
Jacovi A, Marasović A, Miller T, Goldberg Y (2021) Formalizing trust in artificial intelligence: prerequisites, causes and goals of human trust in AI. In: FAccT 2021—proc 2021 ACM conf. fairness, accountability, transpar, Section 2, pp 624–635
Jain A, Ravula M, Ghosh J (2020) Biased models have biased explanations. arXiv2012.10986
Japkowicz N (2006) Why question machine learning evaluation methods. In: AAAI workshop on evaluation methods for machine learning, pp 6–11
Ji M, Yu G, Xi H, Xu T, Qin Y (2021) Measures of success of computerized clinical decision support systems: An overview of systematic reviews. Heal Policy Technol 10(1):196–208
Jie B, Liu M, Liu J, Zhang D, Shen D (2017) Temporally constrained group sparse learning for longitudinal data analysis in alzheimer’s disease. IEEE Trans Biomed Eng 64(1):238–249
Jiménez-Mesa C et al. (2021) Deep learning in current neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability
Jin M, Deng W (2018) Predication of different stages of Alzheimer’s disease using neighborhood component analysis and ensemble decision tree. J Neurosci Methods 302:35–41
Jo T, Nho K, Saykin AJ (2019) Deep learning in alzheimer ’ s disease : diagnostic classification and prognostic prediction using neuroimaging data”. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2019.00220
Jo T, Nho K, Risacher SL, Saykin AJ (2020) Deep learning detection of informative features in tau PET for Alzheimer’s disease classification. BMC Bioinform 21(21):1–14
Joshi G, Walambe R, Kotecha K (2021) A review on explainability in multimodal deep neural nets. IEEE Access 9:59800–59821
Ju R, Hu C, Li Q (2017) Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans Comput Biol Bioinforma 16(1):244–257
Juraev F, El-Sappagh S, Abdukhamidov E, Ali F, Abuhmed T (2022) Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients. J Biomed Inform 135:104216
Kamiran F, Calders T (2012) Data preprocessing techniques for classification without discrimination. Knowl Inf Syst 33(1):1–33
Kamishima T, Akaho S, Asoh H, Sakuma J (2012) Fairness-aware classifier with prejudice remover regularizer. In Joint European conference on machine learning and knowledge discovery in databases, pp 35–50
Katell M et al (2020) Toward situated interventions for algorithmic equity: lessons from the field. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 45–55
Katzir Z, Elovici Y (2018) Quantifying the resilience of machine learning classifiers used for cyber security. Expert Syst Appl 92:419–429
Kaur D, Uslu S, Rittichier KJ, Durresi A (2023) Trustworthy artificial intelligence: a review. ACM Comput Surv 55(2):105–115
Keane MT, Kenny EM (2019) The twin-system approach as one generic solution for XAI: an overview of ANN-CBR twins for explaining deep learning. arXiv1905.08069
Khatami SG et al (2020) Challenges of integrative disease modeling in Alzheimer ’ s disease. Front Mol Biosci 6:1–13
Khedher L, Ramírez J, Górriz J, Brahim A (2015) Early diagnosis of Alzheimer’s disease based on partial least, squares principal componentanalysis and support vector machine using segmented MRI images. Neurocomputing 151:139–150
Khvostikov A, Aderghal K, Krylov A, Catheline G, Benois-Pineau J (2018) 3D inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer’s disease diagnostics. arXiv:1809.03972
Kim HW, Lee HE, Lee S, Oh KT, Yun M, Yoo SK (2020) Slice-selective learning for Alzheimer’s disease classification using a generative adversarial network: a feasibility study of external validation. Eur J Nucl Med Mol Imaging 47(9):2197–2206
Kim MP, Reingold O, Rothblum GN (2018) Fairness through computationally-bounded awareness. arXiv1803.03239
Kindermans P-J, et al (2017) Learning how to explain neural networks: patternnet and patternattribution. arXiv1705.05598
Kleinberg J, Mullainathan S, Raghavan M (2016) Inherent trade-offs in the fair determination of risk scores. arXiv1609.05807
Kovalchuk SV, Kopanitsa GD, Derevitskii IV, Savitskaya DA (2020) Three-stage intelligent support of clinical decision making for higher trust, validity, and explainability. J Biomed Inform 127:1–23
Krasanakis E, Spyromitros-Xioufis E, Papadopoulos S, Kompatsiaris Y (2018) Adaptive sensitive reweighting to mitigate bias in fairness-aware classification. In: Proceedings of the 2018 World Wide Web conference, pp 853–862
Krawczyk B (2016) Learning from imbalanced data: open challenges and future directions. Prog Artif Intell 5(4):221–232
Kuo KL, Fuh CS (2011) A rule-based clinical decision model to support interpretation of multiple data in health examinations. J Med Syst 35(6):1359–1373
Kurakin A, Goodfellow I, Bengio S (2018) Adversarial examples in the physical world. In: Secur AIS (ed) RV yampolskiy. Chapman and Hall/CRC, Boca Raton, pp 99–112
Kuznetsov SO (2001) Machine learning on the basis of formal concept analysis. Autom Remote Control 62(10):1543–1564
Kwon BC et al (2019) RetainVis: visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Trans vis Comput Graph 25(1):299–309
Lamy JB, Sekar B, Guezennec G, Bouaud J, Séroussi B (2019) Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach. Artif Intell Med 94:42–53
Lazli L, Boukadoum M (1894) A survey on computer-aided diagnosis of brain disorders through MRI based on machine learning and data mining methodologies with an emphasis on alzheimer disease diagnosis and the contribution of the multimodal fusion. Appl Sci 10:2020
Lebedev AV et al (2014) Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage Clin 6:115–125
Ledley RS, Lusted LB (1959) Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason. Nucl Phys 13(1):104–116
Lee G et al (2019) Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Sci Rep 9(1):1–12
Lei B, Hou W, Zou W, Li X, Zhang C, Wang T (2019) Longitudinal score prediction for Alzheimer’s disease based on ensemble correntropy and spatial–temporal constraint. Brain Imaging Behav 13(1):126–137
Lei B et al (2020) Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer’s disease. Med Image Anal 61:101652
Lei B et al (2020) Deep and joint learning of longitudinal data for Alzheimer’s disease prediction. Pattern Recognit, vol 102
Lekadir K, et al (2021) FUTURE-AI: guiding principles and consensus recommendations for trustworthy artificial intelligence in medical imaging
Li H, Wang X, Ding S (2018) Research and development of neural network ensembles: a survey. Artif Intell Rev 49(4):455–479
Li K, Wu Z, Peng K-C, Ernst J, Fu Y (2019a) Guided attention inference network. IEEE Trans Pattern Anal Mach Intell 42(12):2996–3010
Li W, Zhao Y, Chen X, Xiao Y, Qin Y (2019b) Detecting Alzheimer’s disease on small dataset: a knowledge transfer perspective. IEEE J Biomed Heal Informatics 23(3):1234–1242
Li W, Lin X, Chen X (2020) Detecting Alzheimer’s disease based on 4D fMRI: an exploration under deep learning framework. Neurocomputing 388:280–287
Li H, Habes M, Wolk DA, Fan Y (2019c) A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal MRI. Alzheimer’s Dement, pp 1–12
Li A, Li F, Elahifasaee F, Liu M, Zhang L (2021) Hippocampal shape and asymmetry analysis by cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Brain Imaging Behav
Li B, et al (2021) Trustworthy AI: from principles to practices
Lian C, Liu M, Zhang J, Shen D (2020a) Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Trans Pattern Anal Mach Intell 42(4):880–893
Lian C, Liu M, Pan Y, Shen D (2020b) Attention-guided hybrid network for dementia diagnosis with structural MR images. IEEE Trans Cybern, pp 1–12
Liang Y, Li S, Yan C, Li M, Jiang C (2021) Explaining the black-box model: a survey of local interpretation methods for deep neural networks. Neurocomputing 419:168–182
Liu L, Caselli RJ (2018) Age stratification corrects bias in estimated hazard of APOE genotype for Alzheimer’s disease. Alzheimer’s Dement Transl Res Clin Interv 4:602–608
Liu F, Zhou L, Shen C, Yin J (2014) Multiple kernel learning in the primal for multimodal alzheimer’s disease classification. IEEE J Biomed Heal Informatics 18(3):984–990
Liu X, Goncalves AR, Cao P, Zhao D, Banerjee A (2018) Modeling Alzheimer’s disease cognitive scores using multi-task sparse group lasso. Comput Med Imaging Graph 66:100–114
Liu Q, Li P, Zhao W, Cai W, Yu S, Leung VCM (2018a) A survey on security threats and defensive techniques of machine learning: a data driven view. IEEE Access 6:12103–12117
Liu M, Cheng D, Wang K, Wang Y (2018b) Multi-modality cascaded convolutional neural networks for alzheimer’s disease diagnosis. Neuroinformatics 16(3–4):295–308
Liu M, Zhang J, Adeli E, Shen D (2018c) Joint classification and regression via deep multi-task multi-channel learning for alzheimer’s disease diagnosis. IEEE Trans Biomed Eng 66:1195–1206
Liu R, Rong Y, Peng Z (2020) A review of medical artificial intelligence. Glob Heal J 4(2):42–45
Liu W, Qiu JL, Zheng WL, Lu BL (2021a) Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition. IEEE Trans Cogn Dev Syst 8920:1–15
Liu Y et al (2021b) Incomplete multi-modal representation learning for Alzheimer’s disease diagnosis. Med Image Anal 69:101953
Liu Y, Radanovic G, Dimitrakakis C, Mandal D, Parkes DC (2017) Calibrated fairness in bandits. arXiv1707.01875
Ljubic B et al (2020) Influence of medical domain knowledge on deep learning for Alzheimer’s disease prediction. Comput Methods Programs Biomed 197:105765
Lorenzi M, Pennec X, Frisoni GB, Ayache N et al (2014) Disentangling normal aging from Alzheimer’s disease in structural MR images. Neurobiol Aging 16(9):801
Lorenzi M, Filippone M, Frisoni GB, Alexander DC (2017) Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer ’ s disease. Neuroimage 190:56–68
Lou Y, Caruana R, Gehrke J, Hooker G (2013) Accurate intelligible models with pairwise interactions. In: Proc. ACM SIGKDD int. conf. knowl. discov. data min, vol Part F1288, pp 623–631
Loureiro SMC, Guerreiro J, Tussyadiah I (2021) Artificial intelligence in business: state of the art and future research agenda. J Bus Res 129:911–926
Lu S, Xia Y, Cai W, Fulham M, Feng DD (2017a) Early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging. Comput Med Imaging Graph 60:35–41
Lu D, Popuri K, Ding GW, Balachandar R, Beg MF (2018a) Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. Med Image Anal 46:26–34
Lu D, Popuri K, Ding W, Balachandar R, Beg MF (2018b) Multimodal and multiscale deep neural networks for the early diagnosis of alzheimer’s disease using structural MR and FDG-PET images. Sci Rep 8(1):5697
Lu MFBD, Popuri K, Ding GW, Balachandar R (2018c) Multimodal and mul- tiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Sci Rep 8(1):5697
Lu J, Issaranon T, Forsyth D (2017b) Safetynet: detecting and rejecting adversarial examples robustly. In: Proceedings of the IEEE international conference on computer vision, pp 446–454
Ma X et al (2021) Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recognit 110:107332
Ma J et al (2022) (2022) Towards trustworthy AI in dentistry. J Dent Res 10:002203452211060
Madaio MA, Stark L, Wortman Vaughan J, Wallach H (2020) Co-designing checklists to understand organizational challenges and opportunities around fairness in AI. In: Conf hum factors comput syst - proc, pp 1–14
Maksymiuk S, Gosiewska A, Biecek P (2020) Landscape of R packages for eXplainable artificial intelligence, vol XX, pp 1–26
Malakar S, Ghosh M, Bhowmik S, Sarkar R, Nasipuri M (2020) A GA based hierarchical feature selection approach for handwritten word recognition. Neural Comput Appl 32(7):2533–2552
Maleki F, Muthukrishnan N, Ovens K, Reinhold C, Forghani R (2020) Machine learning algorithm validation: from essentials to advanced applications and implications for regulatory certification and deployment. Neuroimaging Clin N Am 30(4):433–445
Marcus G, Davis E (2019) Rebooting AI: Building artificial intelligence we can trust. Vintage, New York
Mariotti E, Alonso-Moral JM, Gatt A (2021) Prometheus: harnessing fuzzy logic and natural language for human-centric explainable artificial intelligence. In: XX Spanish congress on fuzzy logic and technologies, pp 274–279
Markus AF, Kors JA, Rijnbeek PR (2021) The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform 113:103655
Martí-Juan G, Sanroma-Guell G, Piella G (2020) A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer’s disease. Comput Methods Programs Biomed 189:105348
McDaniel P, Papernot N, Celik ZB (2016) Machine learning in adversarial settings. IEEE Secur Priv 14(3):68–72
McDermott MBA, Wang S, Marinsek N, Ranganath R, Foschini L, Ghassemi M (2021) Reproducibility in machine learning for health research: still a ways to go. Sci Transl Med. https://doi.org/10.1126/scitranslmed.abb1655
McGill School of Computer Science (2020) The machine learning paper paper reproducibility checklist, pp 0–1
McGraw G, Bonett R, Figueroa H, Shepardson V (2019) Security engineering for machine learning. Computer 52(8):54–57
McLachlan S, Dube K, Hitman GA, Fenton NE, Kyrimi E (2020) Bayesian networks in healthcare: Distribution by medical condition. Artif Intell Med 107:101912
Mehdipour-Ghazi M et al (2019) Training recurrent neural networks robust to incomplete data: application to Alzheimer’s disease progression modeling. Med Image Anal 53:39–46
Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2019) A survey on bias and fairness in machine learning. arXiv
Mendelson AF, Zuluaga MA, Lorenzi M, Hutton BF (2017) Selection bias in the reported performances of AD classification pipelines. NeuroImage Clin 14:400–416
Middleton B, Sittig DF, Wright A (2016) Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb Med Inform 1:S103–S116
Miller T (2019) Explanation in artificial intelligence: Insights from the social sciences. Artif Intell 267:1–38
Minhas S, Khanum A, Riaz F, Khan SA, Alvi A (2017) Predicting progression from mild cognitive impairment to Alzheimer’s disease using autoregressive modelling of longitudinal and multimodal biomarkers. IEEE J Biomed Heal Informatics 22(3):818–825
Mitchell M, et al (2019) Model cards for model reporting. In: FAT* 2019: proc. 2019 conf fairness, accountability, transpar., no Figure 2, pp 220–229
Moher D, Liberati A, Tetzlaff J, Altman DG, Group P (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097
Mohseni S, Zarei N, Ragan ED (2018) A multidisciplinary survey and framework for design and evaluation of explainable AI systems. arXiv1811.11839
Molnar C (2018) Interpretable machine learning: a guide for making black box models explainable. Leanpub
Moosavi-Dezfooli S-M, Fawzi A, Frossard P (2016) Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2574–2582
Moosavi-Dezfooli S-M, Fawzi A, Fawzi O, Frossard P (2017) Universal adversarial perturbations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1765–1773
Moustakidis S, Papandrianos NI, Christodolou E, Papageorgiou E, Tsaopoulos D (2020) Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness. Neural Comput Appl 5
Muhammed-Niyas KP, Thiyagarajan P (2021) Alzheimer’s classification using dynamic ensemble of classifiers selection algorithms: a performance analysis. Biomed Signal Process Control 68:102729
Bacanin Ruxandra N-S, Zivkovic M, Petrovic, A, Rashid, TA, Bezdan T (2021) Performance of a novel chaotic firefly algorithm with enhanced exploration for tackling global optimization problems: application for dropout regula, “performance of a novel chaotic firefly algorithm with enhanced exploration for tackling global optimization problems: application for dropout regularization. Mathematics 9(21): 2705
Nanni L, Lumini A, Zaffonato N (2018) Ensemble based on static classifier selection for automated diagnosis of mild cognitive impairment. J Neurosci Methods 302:42–46
Narayanan A (2018) Translation tutorial: 21 fairness definitions and their politics. In: Proc. conf. fairness accountability transp, New York, USA, 2018, vol 2, no 3, pp 2–6
Narayanan A, Shmatikov V (2008) Robust de-anonymization of large datasets (how to break anonymity of the netflix prize dataset). In: University of Texas at Austin
Nasiri S, Zahedi G, Kuntz S, Fathi M (2019) Knowledge representation and management based on an ontological CBR system for dementia caregiving. Neurocomputing 350:181–194
Neri E, Coppola F, Miele V, Bibbolino C, Grassi R (2020) Artificial intelligence: who is responsible for the diagnosis? Radiol Medica 125(6):517–521
Nguyen L, Wang S, Sinha A (2018) A learning and masking approach to secure learning. In International conference on decision and game theory for security, pp 453–464
Nicolae MI et al (2018) Adversarial robustness toolbox v0.4.0. arXiv, 1–34
Nie L, Zhang L, Meng L, Song X, Chang X, Li X (2017) Modeling disease progression via multisource multitask learners: a case study with alzheimer’s disease. IEEE Trans Neural Networks Learn Syst 28(7):1508–1519
Nordberg A, Rinne JO, Kadir A, Långström B (2010) The use of PET in Alzheimer disease. Nat Rev Neurol 6(2):78–87
Oh K, Chung YC, Kim KW, Kim WS, Oh IS (2019) Classification and visualization of alzheimer’s disease using volumetric convolutional neural network and transfer learning. Sci Rep 9(1):1–16
Olah C et al (2018) The building blocks of interpretability. Distill 3(3):e10
On R (2019) High-level expert group on artificial intelligence ethics guidelines for trustworthy AI. Eur Commun, 09(04)
P 1607-U GOES Procedures (1978) Code of federal regulations
Page MJ et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. J Clin Epidemiol 134:178–189
Pan X, Adel M, Fossati C, Gaidon T, Guedj E (2019) Multilevel feature representation of FDG-PET brain images for diagnosing alzheimer’s disease. IEEE J Biomed Heal Informatics 23(4):1499–1506
Pan X et al (2021) Multi-view separable pyramid network for AD prediction at MCI stage by 18F-FDG brain PET imaging. IEEE Trans Med Imaging 40(1):81–92
Pang T, Xu K, Du C, Chen N, Zhu J (2019) Improving adversarial robustness via promoting ensemble diversity. In: 36th Int. conf. mach. learn. ICML 2019, vol 2019-June, pp 8759–8771
Papangelou K, Sechidis K, Weatherall J, Brown G (2018) Toward an understanding of adversarial examples in clinical trials. In Joint European conference on machine learning and knowledge discovery in databases, pp 35–51
Papernot N, McDaniel P (2018) Deep k-nearest neighbors: towards confident, interpretable and robust deep learning. arXiv1803.04765
Papernot N, Song S, Mironov I, Raghunathan A, Talwar K, Erlingsson Ú (2018) Scalable private learning with pate. arXiv1802.08908
Park JH, et al (2020) Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data. npj Digit Med
Pellegrini E et al (2018) Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review. Alzheimer’s Dement 10:519–535
Peng J, Zhu X, Wang Y, An L, Shen D (2019) Structured sparsity regularized multiple kernel learning for Alzheimer’s disease diagnosis. Pattern Recognit 88:370–382
Pesapane F et al (2020) Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists. Med Oncol 37(5):1–9
Pineau J, et al (2020) Improving reproducibility in machine learning research (a report from the NeurIPS 2019 reproducibility program)
Pitropakis N, Panaousis E, Giannetsos T, Anastasiadis E, Loukas G (2019) A taxonomy and survey of attacks against machine learning. Comput. Sci. Rev. 34:100199
Platero C, Lin L, Tobar MC (2019) Longitudinal neuroimaging hippocampal markers for diagnosing alzheimer’s disease. Neuroinformatics 17(1):43–61
Poloni KM, Duarte de Oliveira IA, Tam R, Ferrari RJ (2021) Brain MR image classification for Alzheimer’s disease diagnosis using structural hippocampal asymmetrical attributes from directional 3-D log-Gabor filter responses. Neurocomputing 419:126–135
Puente-Castro A, Fernandez-Blanco E, Pazos A, Munteanu CR (2020) Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques”. Comput Biol Med 120:103764
Qayyum A, Qadir J, Bilal M, Al-Fuqaha A (2021) Secure and Robust machine learning for healthcare: a survey. IEEE Rev Biomed Eng 14:156–180
Qiu S, Chang GH, Panagia M, Gopal DM, Au R, Kolachalama VB (2018) Fusion of deep learning models of MRI scans, Mini-Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimer’s Dement. Diagnosis. Assess. Dis. Monit. 10:737–749
Qiu S et al (2020) Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain 143(6):1920–1933
Rajani NF, Mooney R (2018) Stacking with auxiliary features for visual question answering. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, vol. 1 (Long Papers), pp 2217–2226
Ramírez J et al (2018) Ensemble of random forests one vs. rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares. J Neurosci Methods 302:47–57
Raschka S (2018) Model evaluation, model selection, and algorithm selection in machine learning
Rashed-Al-Mahfuz M, Haque A, Azad A, Alyami SA, Quinn JMW, Moni MA (2021) Clinically applicable machine learning approaches to identify attributes of chronic kidney disease (CKD) for use in low-cost diagnostic screening. IEEE J Transl Eng Heal Med 9:1–11
Rathore C, Habes S, Iftikhar M, Shacklett MA, Davatzikos A (2017) A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage 155:530–548
Regulation (EU) and 2016/679, European Union (2016) general data protection regulation
Rémi-Cuingnet O, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert M-O, Chupin M, Benali H et al (2011) Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56(2):766–781
Ribeiro MT, Singh MT, Guestrin C (2016) Why should I trust you?’: explaining the predictions of any classifier
Richards et al (2018) Bidirectional RNN for medical event detection in electronic health records. Physiol Behav 176(5):139–148
Richhariya B, Tanveer M, Rashid AH, Neuroimaging D (2020) Biomedical signal processing and control diagnosis of alzheimer ’ s disease using universum support vector machine based recursive feature elimination ( USVM-RFE ), vol 59
Riley KP, Snowdon DA, Desrosiers MF (2005) Early life linguisticability, late life cognitive function, and neuropathology: findings from the Nun study. Neurobiol Aging 26(3):341–347
Rojat T, Puget R, Filliat D, Del Ser J, Gelin R, Díaz-Rodríguez N (2021) Explainable artificial intelligence (XAI) on TimeSeries data: a survey
Routier A, Bottani S, Dormont D, Burgos N, Colliot O Convolutional neural networks for classification of alzheimer’s disease: overview and reproducible evaluation.”
Ruan W, Wu M, Sun Y, Huang X, Kroening D, Kwiatkowska M (2019) Global robustness evaluation of deep neural networks with provable guarantees for the hamming distance
Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, vol 1, no 5
S V-L, Van DerVlies R (2021) Trustworthy AI in education: promises and challenges
Sagi O, Rokach L (2018) Ensemble learning: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4):1–18
Saleiro P et al (2018) Aequitas: a bias and fairness audit toolkit. arXiv
Salimi B, Howe B, Suciu D (2019) Data management for causal algorithmic fairness. arXiv1908.07924
Saluja R, Malhi A, Knapič S, Främling K, Cavdar C (2021) Towards a rigorous evaluation of explainability for multivariate time series
Samangouei P, Kabkab M, Chellappa R (2018) Defense-GAN: Protecting classifiers against adversarial attacks using generative models. arXiv1805.06605
Samper-González J et al (2018) Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data. Neuroimage 183(July):504–521
Sanchez-Martinez S et al (2019) Machine learning for clinical decision-making: challenges and opportunities. Preprints 2019110278:1–38
Sarraf GTS (2016) DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. J BioRxiv 070441:070441
Schneeberger D, Stöger K, Holzinger A (2020) The European legal framework for medical AI. In: Lect notes comput sci (including subser. lect. notes artif. intell. lect. notes bioinformatics), vol 12279 LNCS, pp 209–226
Seo K, Pan R, Lee D, Thiyyagura P, Chen K (2019) Visualizing Alzheimer’s disease progression in low dimensional manifolds. Heliyon 5(8):e02216
Shen HT et al (2021) Heterogeneous data fusion for predicting mild cognitive impairment conversion. Inf. Fusion 66:54–63
Shneiderman B (2020) Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Trans. Interact. Intell. Syst. 10(4):1–31
Shoaip N, Rezk A, El-Sappagh S, Alarabi L, Barakat S, Elmogy MM (2020) A comprehensive fuzzy ontology-based decision support system for alzheimer’s disease diagnosis. IEEE Access 9:31350–31372
Shrikumar A, Greenside P, Shcherbina A, Kundaje A (2016) Not just a black box: Learning important features through propagating activation differences. arXiv1605.01713
Singh N, Singh P (2020) Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus. Biocybern Biomed Eng 40(1):1–22
Singh A, Sengupta S, Lakshminarayanan V (2020) Explainable deep learning models in medical image analysis. J Imaging 6(6):1–18
Skillen KL, Chen L, Nugent CD, Donnelly MP, Burns W, Solheim I (2014) Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments. Futur Gener Comput Syst 34:97–109
Song Y, Kim T, Nowozin S, Ermon S, Kushman N (2017) Pixeldefend: Leveraging generative models to understand and defend against adversarial examples. arXiv1710.10766
Sørensen L et al (2017) Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. NeuroImage Clin 13:470–482
Štrumbelj E, Kononenko I (2010) An efficient explanation of individual classifications using game theory. J Mach Learn Res 11:1–18
Su G, Wei D, Varshney KR, Malioutov DM (2016) Interpretable two-level Boolean rule learning for classification
Su D, Zhang H, Chen H, Yi J, Chen PY, Gao Y (2018) Is robustness the cost of accuracy?—a comprehensive study on the robustness of 18 deep image classification models. In: Lect notes comput sci (including subser lect notes artif intell lect notes bioinformatics), LNCS 11216:644–661
Su KSJ, Vargas DV (2019) One pixel attack for fooling deep neural networks. IEEE Trans Evol Comput 23(5):828–841
Suk DSH-I, Lee S-W (2014) Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 101:569–582
Suk DSH-I, Lee S-W (2015) Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct 220(2):841–859
Suryanarayanan P et al (2020) A canonical architecture for predictive analytics on longitudinal patient records. arXiv
Syed AH, Khan T, Hassan A, Alromema NA, Binsawad M, Alsayed AO (2020) An ensemble-learning based application to predict the earlier stages of alzheimer’s disease (AD). IEEE Access 8:222126–222143
Tabarestani S et al (2020) A distributed multitask multimodal approach for the prediction of Alzheimer’s disease in a longitudinal study. Neuroimage 206:116317
Tanveer M, Richhariya B, Khan RU, Rashid AH (2020) Machine learning techniques for the diagnosis of alzheimer’s disease: a review. ACM Trans Multimed Comput Commun Appl 16:1–35
Tatman R, Vanderplas J, Dane S (2018) A practical taxonomy of reproducibility for machine learning research. Rml@Icml 2018
Thiebes S, Lins S, Sunyaev A (2021) Trustworthy artificial intelligence. Electron Mark 31(2):447–464
Tong T, Gao Q, Guerrero R, Ledig C, Chen L, Rueckert D (2017) A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer’s disease. IEEE Trans Biomed Eng 64(1):155–165
Toreini E, Aitken M, Coopamootoo K, Elliott K, Zelaya CG, van Moorsel A (2020) The relationship between trust in AI and trustworthy machine learning technologies. In: FAT* 2020—proc. 2020 conf. fairness, accountability, transpar, 272–283
Toreini E et al (2020) Technologies for trustworthy machine learning: a survey in a socio-technical context. arXiv
Tramer F, et al (2017) Fairtest: discovering unwarranted associations in data-driven applications. In IEEE European symposium on security and privacy (EuroS&P), 401 416
Tramèr F, Kurakin A, Papernot N, Goodfellow I, Boneh D, McDaniel P (2017) Ensemble adversarial training: Attacks and defences. arXiv1705.07204
Üstün B, Melssen WJ, Buydens LMC (2007) Visualisation and interpretation of Support Vector Regression models. Anal Chim Acta 595(1–2):299–309
Vaithinathan K, Parthiban L (2019) A novel texture extraction technique with T1 weighted MRI for the classification of alzheimer’s disease. J Neurosci Methods 318(January):84–99
Varghese T, Sheelakumari R, James JS (2013) A review of neuroimaging biomarkers of Alzheimer’s disease. Neurol Asia 18(3):239–248
Varona D, Lizama-Mue Y, Suárez JL (2021) Machine learning’s limitations in avoiding automation of bias. AI Soc 36(1):197–203
Vemuri P, Jack Jr R (2010) Role of structural MRI in Alzheimer’s disease. Alzheimer’s Res Ther 2:23
Verma S, Rubin J (2018) Fairness definitions explained. In: Proc—int conf softw eng, pp 1–7
Vesnic-Alujevic L, Nascimento S, Pólvora A (2020) Societal and ethical impacts of artificial intelligence: Critical notes on European policy frameworks. Telecomm. Policy 44(6):101961
Wang X, Li J, Kuang X, Tan Y, Li J (2019a) The security of machine learning in an adversarial setting: a survey. J Parallel Distrib Comput 130:12–23
Wang H et al (2019c) Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing 333:145–156
Wang M, Lian C, Yao D, Zhang D, Liu M, Member S (2019d) Spatial-temporal dependency modeling and network hub detection for functional MRI analysis via convolutional-recurrent network. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2019.2957921
Wang M, Zhang D, Shen D, Liu M (2019e) Multi-task exclusive relationship learning for alzheimer’s disease progression prediction with longitudinal data. Med Image Anal 53:111–122
Wang S, Wang Y, Wang D, Yin Y, Wang Y, Jin Y (2020a) An improved random forest-based rule extraction method for breast cancer diagnosis. Appl Soft Comput J 86:105941
Wang L, Liu Y, Zeng X, Cheng H, Wang Z, Wang Q (2020b) Region-of-Interest based sparse feature learning method for Alzheimer’s disease identification. Comput Methods Programs Biomed 187:105290
Wang J, Sun J, Zhang P, Wang X (2018a) Detecting adversarial samples for deep neural networks through mutation testing. arXiv1805.05010
Wang T, Qiu RG, Yu M (2018b) Predictive modeling of the progression of alzheimer ’ s disease with recurrent neural networks. Sci Rep, pp 1–12
Wang Y-X, Balle B, Kasiviswanathan SP (2019b) Subsampled Rényi differential privacy and analytical moments accountant. In: The 22nd international conference on artificial intelligence and statistics, pp 1226–1235
Wang D et al. (2021) ‘Brilliant AI doctor’ in rural China: tensions and challenges in AI-powered CDSS deployment. arXiv:2101.01524v2
Weiner MW et al (2017) Recent publications from the Alzheimer’s disease neuroimaging initiative: reviewing progress toward improved AD clinical trials. Alzheimer’s Dement 13(4):e1–e85
Wen J et al (2020) Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Med Image Anal 63:101694
Wen J et al (2021) Reproducible evaluation of diffusion MRI features for automatic classification of patients with alzheimer’s disease. Neuroinformatics 19(1):57–78
Wexler J, Pushkarna M, Bolukbasi T, Wattenberg M, Viégas F, Wilson J (2019) The what-if tool: Interactive probing of machine learning models. IEEE Trans vis Comput Graph 26(1):56–65
Wiens J et al (2019) Do no harm: a roadmap for responsible machine learning for health care. Nat Med 25:1337–1340
Williams JP, Storlie CB, Therneau TM, Jr CRJ, Hannig J (2020) A Bayesian approach to multistate hidden Markov models: application to dementia progression. J Am Stat Assoc 115(529):16–31
Writer ND, Ahmed S, Bajema NE, Bendett S, Chang BA, et al (2019) Artificial intelligence, China, Russia, and the global order technological, political, global, and creative perspectives
Wiśniewski J, Biecek P (2021a) Fairmodels: a flexible tool for bias detection, visualization, and mitigation. 1–15
Wiśniewski J, Biecek P (2021b) Fairmodels: a flexible tool for bias detection, visualization, and mitigation. arXiv2104.00507
Xiao R, Cui X, Qiao H, Zheng X, Zhang Y (2021) Early diagnosis model of Alzheimer’s disease based on sparse logistic regression. Multimed Tools Appl 80(3):3969–3980
Xiong P, Buffett S, Iqbal S, Lamontagne P, Mamun M, Molyneaux H (2021) Towards a Robust and trustworthy machine learning system development. J ACM
Xu M, Zhang T, Li Z, Liu M, Zhang D (2021) Towards evaluating the robustness of deep diagnostic models by adversarial attack. Med Image Anal 69:101977
Xu W, Evans D, Qi Y (2017) Feature squeezing: detecting adversarial examples in deep neural networks. arXiv1704.01155
Xu K, et al (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International conference on machine learning, 2015, pp 2048–2057
Yamanakkanavar N, Choi JY, Lee B (2020) MRI segmentation and classification of human brain using deep learning for diagnosis of alzheimer’s disease : a survey. Sensors 20:3243
Yan JN, Gu Z, Lin H, Rzeszotarski JM (2002) Silva: interactively assessing machine learning fairness using causality. In: Conf. Hum. Factors comput. Syst. - proc.
Yang L, Shami A (2020) On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415:295–316
Yao D, Calhoun VD, Fu Z, Du Y, Sui J (2018) An ensemble learning system for a 4-way classification of Alzheimer’s disease and mild cognitive impairment. J Neurosci Methods 302:75–81
Yu T, Zhu H (2020) Hyper-parameter optimization: a review of algorithms and applications. arXiv, pp 1–56
Yuan X, He P, Zhu Q, Li X (2019) Adversarial examples: attacks and defenses for deep learning. IEEE Trans Neural Networks Learn Syst 30(9):2805–2824
Yue L, Gong X, Li J, Ji H, Li M, Nandi AK (2019) Hierarchical feature extraction for early Alzheimer’s disease diagnosis. IEEE Access 7:93752–93760
Zeiler MD, Taylor GW, Fergus R (2011) Adaptive deconvolutional networks for mid and high level feature learning. In: International conference on computer vision, 2011, pp 2018–2025
Zeng N, Qiu H, Wang Z, Liu W, Zhang H, Li Y (2018) A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing 320:195–202
Zhang R, Simon G, Yu F (2017) Advancing Alzheimer’s research: a review of big data promises. Int J Med Inform 106(July):48–56
Zhang F, Li Z, Zhang B, Du H, Wang B, Zhang X (2019b) Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease. Neurocomputing 361:185–195
Zhang Y, Wang S, Xia K, Jiang Y, Qian P (2021) Alzheimer’s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion. Inf. Fusion 66:170–183
Zhang X, Han L, Zhu W, Sun L, Zhang D (2021) An explainable 3D residual self-attention deep neural network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE J Biomed Heal Inform 14(8):5289–5297
Zhang Y, Zhou L (2019) Fairness Assessment for Artificial Intelligence in Financial Industry. arXiv:1912.07211v1
Zhang X, Wei Y, Feng J, Yang Y, Huang TS (2018) Adversarial complementary learning for weakly supervised object localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1325–1334
Zhang JM, Harman M, Ma L, Liu Y (2019a) Machine learning testing: Survey, landscapes and horizons. arXiv
Zhao K et al (2020) Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease: diagnosis, longitudinal progress and biological basis. Sci Bull 65(13):1103–1113
Zhao Y, Jiang P, Zeng D, Wang X, Li S (2021) Prediction of Alzheimer’s disease progression with multi-information generative. IEEE J Biomed Heal INFORMATICS 25(3):711–719
Zhou J, Liu J, Narayan VA, Ye J (2013) Modeling disease progression via multi-task learning. Neuroimage 78:233–248
Zhu X, Il-Suk H, Lee SW, Shen D (2019) Discriminative self-representation sparse regression for neuroimaging-based alzheimer’s disease diagnosis. Brain Imaging Behav 13(1):27–40
Zhu Y, Kim M, Zhu X, Kaufer D, Wu G (2021) Long range early diagnosis of Alzheimer’s disease using longitudinal MR imaging data. Med Image Anal 67:101825
Zicari RV, et al. (2021a) On assessing trustworthy AI in healthcare. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Front Hum Dyn, p 30.
Zicari RV et al (2021b) Z—inspection ®: a process to assess trustworthy AI
Zilke JR, Mencía EL, Janssen F (2016) Deepred–rule extraction from deep neural networks. In: International conference on discovery science, pp 457–473
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1A2C1011198), (Institute for Information & communications Technology Planning & Evaluation) (IITP) grant funded by the Korea government (MSIT) under the ICT Creative Consilience Program (IITP-2021-2020-0-01821), and AI Platform to Fully Adapt and Reflect Privacy-Policy Changes (No. 2022-0-00688). This research was funded by the Spanish Ministry for Science and Innovation (grants PID2020-112623GB-I00, PDC2021-121072-C21, PID2021-123152OB-C21 and TED2021-130295B-C33) and the Galician Ministry of Education, University and Professional Training (grants ED431C2022/19, and ED431G2019/04). All these grants were co-funded by the European Regional Development Fund (ERDF/FEDER program).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
El-Sappagh, S., Alonso-Moral, J.M., Abuhmed, T. et al. Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges. Artif Intell Rev 56, 11149–11296 (2023). https://doi.org/10.1007/s10462-023-10415-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-023-10415-5