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Timeline of important events related to research and development of nanomedicine, AI-based methods, and their applications in advancements of nanomedicine. This timeline (1930–2024) illustrates the progression of advancements in machine learning and deep learning methods as highlighted in blue boxes in the left upper panel. Additionally, the most relevant milestones in nanomedicine are highlighted in green boxes in the left lower panel. Since 2014, a growing integration between the two disciplines has been observed, especially in key areas such as biomarker identifications and diagnosis, methodological or computational development, prognosis, and therapy or drug delivery for the management of brain disorders are highlighted in orange box in the right upper and lower panel. AD, Alzheimer’s Disease; ANN, Artificial Neural Network; CNN, Convolutional Neural Network; DL, Deep Learning; <t>EEG,</t> Electroencephalogram; EGFR, Epidermal Growth Factor Receptor; EV, Extracellular Vesicles; GAN, Generative Adversarial Network; KNN, K-Nearest Neighbors; LDA, Linear Discriminant Analysis; ML, Machine Learning; MRI, Magnetic Resonance Imaging; MS, Multiple Sclerosis; NP, Nanoparticle; OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis; <t>PD,</t> <t>Parkinson’s</t> Disease; RF, Random Forest; SEM, Structural Equation Modeling; SERS, Surface-Enhanced Raman Spectroscopy; SVM, Support Vector Machine.
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schizophrenia eeg dataset  (Kaggle Inc)
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Timeline of important events related to research and development of nanomedicine, AI-based methods, and their applications in advancements of nanomedicine. This timeline (1930–2024) illustrates the progression of advancements in machine learning and deep learning methods as highlighted in blue boxes in the left upper panel. Additionally, the most relevant milestones in nanomedicine are highlighted in green boxes in the left lower panel. Since 2014, a growing integration between the two disciplines has been observed, especially in key areas such as biomarker identifications and diagnosis, methodological or computational development, prognosis, and therapy or drug delivery for the management of brain disorders are highlighted in orange box in the right upper and lower panel. AD, Alzheimer’s Disease; ANN, Artificial Neural Network; CNN, Convolutional Neural Network; DL, Deep Learning; <t>EEG,</t> Electroencephalogram; EGFR, Epidermal Growth Factor Receptor; EV, Extracellular Vesicles; GAN, Generative Adversarial Network; KNN, K-Nearest Neighbors; LDA, Linear Discriminant Analysis; ML, Machine Learning; MRI, Magnetic Resonance Imaging; MS, Multiple Sclerosis; NP, Nanoparticle; OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis; <t>PD,</t> <t>Parkinson’s</t> Disease; RF, Random Forest; SEM, Structural Equation Modeling; SERS, Surface-Enhanced Raman Spectroscopy; SVM, Support Vector Machine.
Schizophrenia Eeg Dataset, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/schizophrenia eeg dataset/product/Kaggle Inc
Average 90 stars, based on 1 article reviews
schizophrenia eeg dataset - by Bioz Stars, 2026-04
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epileptic eeg datasets  (Kaggle Inc)
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Kaggle Inc epileptic eeg datasets
Timeline of important events related to research and development of nanomedicine, AI-based methods, and their applications in advancements of nanomedicine. This timeline (1930–2024) illustrates the progression of advancements in machine learning and deep learning methods as highlighted in blue boxes in the left upper panel. Additionally, the most relevant milestones in nanomedicine are highlighted in green boxes in the left lower panel. Since 2014, a growing integration between the two disciplines has been observed, especially in key areas such as biomarker identifications and diagnosis, methodological or computational development, prognosis, and therapy or drug delivery for the management of brain disorders are highlighted in orange box in the right upper and lower panel. AD, Alzheimer’s Disease; ANN, Artificial Neural Network; CNN, Convolutional Neural Network; DL, Deep Learning; <t>EEG,</t> Electroencephalogram; EGFR, Epidermal Growth Factor Receptor; EV, Extracellular Vesicles; GAN, Generative Adversarial Network; KNN, K-Nearest Neighbors; LDA, Linear Discriminant Analysis; ML, Machine Learning; MRI, Magnetic Resonance Imaging; MS, Multiple Sclerosis; NP, Nanoparticle; OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis; <t>PD,</t> <t>Parkinson’s</t> Disease; RF, Random Forest; SEM, Structural Equation Modeling; SERS, Surface-Enhanced Raman Spectroscopy; SVM, Support Vector Machine.
Epileptic Eeg Datasets, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/epileptic eeg datasets/product/Kaggle Inc
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epileptic eeg datasets - by Bioz Stars, 2026-04
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Timeline of important events related to research and development of nanomedicine, AI-based methods, and their applications in advancements of nanomedicine. This timeline (1930–2024) illustrates the progression of advancements in machine learning and deep learning methods as highlighted in blue boxes in the left upper panel. Additionally, the most relevant milestones in nanomedicine are highlighted in green boxes in the left lower panel. Since 2014, a growing integration between the two disciplines has been observed, especially in key areas such as biomarker identifications and diagnosis, methodological or computational development, prognosis, and therapy or drug delivery for the management of brain disorders are highlighted in orange box in the right upper and lower panel. AD, Alzheimer’s Disease; ANN, Artificial Neural Network; CNN, Convolutional Neural Network; DL, Deep Learning; EEG, Electroencephalogram; EGFR, Epidermal Growth Factor Receptor; EV, Extracellular Vesicles; GAN, Generative Adversarial Network; KNN, K-Nearest Neighbors; LDA, Linear Discriminant Analysis; ML, Machine Learning; MRI, Magnetic Resonance Imaging; MS, Multiple Sclerosis; NP, Nanoparticle; OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis; PD, Parkinson’s Disease; RF, Random Forest; SEM, Structural Equation Modeling; SERS, Surface-Enhanced Raman Spectroscopy; SVM, Support Vector Machine.

Journal: Frontiers in Medicine

Article Title: Artificial intelligence based advancements in nanomedicine for brain disorder management: an updated narrative review

doi: 10.3389/fmed.2025.1599340

Figure Lengend Snippet: Timeline of important events related to research and development of nanomedicine, AI-based methods, and their applications in advancements of nanomedicine. This timeline (1930–2024) illustrates the progression of advancements in machine learning and deep learning methods as highlighted in blue boxes in the left upper panel. Additionally, the most relevant milestones in nanomedicine are highlighted in green boxes in the left lower panel. Since 2014, a growing integration between the two disciplines has been observed, especially in key areas such as biomarker identifications and diagnosis, methodological or computational development, prognosis, and therapy or drug delivery for the management of brain disorders are highlighted in orange box in the right upper and lower panel. AD, Alzheimer’s Disease; ANN, Artificial Neural Network; CNN, Convolutional Neural Network; DL, Deep Learning; EEG, Electroencephalogram; EGFR, Epidermal Growth Factor Receptor; EV, Extracellular Vesicles; GAN, Generative Adversarial Network; KNN, K-Nearest Neighbors; LDA, Linear Discriminant Analysis; ML, Machine Learning; MRI, Magnetic Resonance Imaging; MS, Multiple Sclerosis; NP, Nanoparticle; OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis; PD, Parkinson’s Disease; RF, Random Forest; SEM, Structural Equation Modeling; SERS, Surface-Enhanced Raman Spectroscopy; SVM, Support Vector Machine.

Article Snippet: They trained machine learning models using EEG datasets from Kaggle and clinical records of Parkinson’s to optimize DBS stimulation parameters.

Techniques: Biomarker Discovery, Magnetic Resonance Imaging, Raman Spectroscopy, Plasmid Preparation