Suchergebnisse - "Artificial Intelligence [MeSH]"
-
1
Autoren:
Quelle: Clinical and Translational Science. 18(10)
Schlagwörter: 3214 Pharmacology and Pharmaceutical Sciences (for-2020), 32 Biomedical and Clinical Sciences (for-2020), Rare Diseases (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Orphan Drug (rcdc), Machine Learning and Artificial Intelligence (rcdc), 5.1 Pharmaceuticals (hrcs-rac), Generic health relevance (hrcs-hc), Humans (mesh), Anticonvulsants (mesh), Drug Monitoring (mesh), Models, Biological (mesh), Artificial Intelligence (mesh), Prohibitins (mesh), Male (mesh), Epilepsy (mesh), Female (mesh), Adult (mesh), Middle Aged (mesh), Valproic Acid (mesh), Phenytoin (mesh), Phenobarbital (mesh), Carbamazepine (mesh), Young Adult (mesh), artificial intelligence, population pharmacokinetic model, predictive performance, Humans (mesh), Epilepsy (mesh), Valproic Acid (mesh), Phenytoin (mesh), Phenobarbital (mesh), Carbamazepine (mesh), Anticonvulsants (mesh), Drug Monitoring (mesh), Models, Biological (mesh), Artificial Intelligence (mesh), Adult (mesh), Middle Aged (mesh), Female (mesh), Male (mesh), Young Adult (mesh), Prohibitins (mesh), artificial intelligence, population pharmacokinetic model, predictive performance, Humans (mesh), Anticonvulsants (mesh), Drug Monitoring (mesh), Models, Biological (mesh), Artificial Intelligence (mesh), Prohibitins (mesh), Male (mesh), Epilepsy (mesh), Female (mesh), Adult (mesh), Middle Aged (mesh), Valproic Acid (mesh), Phenytoin (mesh), Phenobarbital (mesh), Carbamazepine (mesh), Young Adult (mesh), 1102 Cardiorespiratory Medicine and Haematology (for), 1112 Oncology and Carcinogenesis (for), 1199 Other Medical and Health Sciences (for), General Clinical Medicine (science-metrix), 3201 Cardiovascular medicine and haematology (for-2020), 3214 Pharmacology and pharmaceutical sciences (for-2020)
Dateibeschreibung: application/pdf
-
2
Autoren: et al.
Quelle: Nature Communications. 16(1)
Schlagwörter: 46 Information and Computing Sciences (for-2020), 32 Biomedical and Clinical Sciences (for-2020), 3211 Oncology and Carcinogenesis (for-2020), Urologic Diseases (rcdc), Machine Learning and Artificial Intelligence (rcdc), Prostate Cancer (rcdc), Aging (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Cancer (rcdc), Humans (mesh), Male (mesh), Prostatic Neoplasms (mesh), Neoplasm Grading (mesh), Artificial Intelligence (mesh), Pathologists (mesh), Observer Variation (mesh), Prostate (mesh), Prostate (mesh), Humans (mesh), Prostatic Neoplasms (mesh), Observer Variation (mesh), Artificial Intelligence (mesh), Male (mesh), Neoplasm Grading (mesh), Pathologists (mesh), Humans (mesh), Male (mesh), Prostatic Neoplasms (mesh), Neoplasm Grading (mesh), Artificial Intelligence (mesh), Pathologists (mesh), Observer Variation (mesh), Prostate (mesh)
Dateibeschreibung: application/pdf
-
3
Autoren: et al.
Quelle: Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology. 17(4)
Schlagwörter: 3206 Medical Biotechnology (for-2020), 32 Biomedical and Clinical Sciences (for-2020), Data Science (rcdc), Biotechnology (rcdc), Bioengineering (rcdc), Cancer (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Nanotechnology (rcdc), Machine Learning and Artificial Intelligence (rcdc), 5.1 Pharmaceuticals (hrcs-rac), Generic health relevance (hrcs-hc), 3 Good Health and Well Being (sdg), Nanomedicine (mesh), Humans (mesh), Machine Learning (mesh), Artificial Intelligence (mesh), Animals (mesh), Nanoparticles (mesh), artificial intelligence, drug delivery, machine learning, nanomedicine, pharmacokinetics, Animals (mesh), Humans (mesh), Artificial Intelligence (mesh), Nanomedicine (mesh), Nanoparticles (mesh), Machine Learning (mesh), artificial intelligence, drug delivery, machine learning, nanomedicine, pharmacokinetics, Nanomedicine (mesh), Humans (mesh), Machine Learning (mesh), Artificial Intelligence (mesh), Animals (mesh), Nanoparticles (mesh), 0304 Medicinal and Biomolecular Chemistry (for), 0903 Biomedical Engineering (for), 1007 Nanotechnology (for), Nanoscience & Nanotechnology (science-metrix), 3206 Medical biotechnology (for-2020), 3404 Medicinal and biomolecular chemistry (for-2020), 4018 Nanotechnology (for-2020)
Dateibeschreibung: application/pdf
-
4
Autoren: et al.
Quelle: Journal of General Internal Medicine. 40(10)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 4203 Health Services and Systems (for-2020), 4206 Public Health (for-2020), 3202 Clinical Sciences (for-2020), 42 Health Sciences (for-2020), Chronic Pain (rcdc), Opioids (rcdc), Drug Abuse (NIDA only) (rcdc), Prevention (rcdc), Machine Learning and Artificial Intelligence (rcdc), Pain Research (rcdc), Behavioral and Social Science (rcdc), Substance Misuse (rcdc), Clinical Research (rcdc), Patient Safety (rcdc), 8.1 Organisation and delivery of services (hrcs-rac), 8.3 Policy, ethics, and research governance (hrcs-rac), Generic health relevance (hrcs-hc), Humans (mesh), Pain Management (mesh), Decision Support Systems, Clinical (mesh), Algorithms (mesh), Analgesics, Opioid (mesh), Drug Industry (mesh), Artificial Intelligence (mesh), pain, opioid industry, clinical decision support, algorithms, opioid risk scores, Humans (mesh), Analgesics, Opioid (mesh), Algorithms (mesh), Drug Industry (mesh), Artificial Intelligence (mesh), Decision Support Systems, Clinical (mesh), Pain Management (mesh), algorithms, clinical decision support, opioid industry, opioid risk scores, pain, Humans (mesh), Pain Management (mesh), Decision Support Systems, Clinical (mesh), Algorithms (mesh), Analgesics, Opioid (mesh), Drug Industry (mesh), Artificial Intelligence (mesh), 1103 Clinical Sciences (for), General & Internal Medicine (science-metrix), 3202 Clinical sciences (for-2020), 4203 Health services and systems (for-2020), 4206 Public health (for-2020)
Dateibeschreibung: application/pdf
-
5
Autoren: et al.
Quelle: JAMA Network Open. 8(7)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3212 Ophthalmology and Optometry (for-2020), Aging (rcdc), Neurosciences (rcdc), Machine Learning and Artificial Intelligence (rcdc), Clinical Research (rcdc), Macular Degeneration (rcdc), Eye Disease and Disorders of Vision (rcdc), Neurodegenerative (rcdc), 4.1 Discovery and preclinical testing of markers and technologies (hrcs-rac), Eye (hrcs-hc), Humans (mesh), Artificial Intelligence (mesh), Workflow (mesh), Macular Degeneration (mesh), Female (mesh), Male (mesh), Aged (mesh), Eye Diseases (mesh), Reproducibility of Results (mesh), Middle Aged (mesh), Humans (mesh), Eye Diseases (mesh), Macular Degeneration (mesh), Reproducibility of Results (mesh), Artificial Intelligence (mesh), Aged (mesh), Middle Aged (mesh), Female (mesh), Male (mesh), Workflow (mesh), Humans (mesh), Artificial Intelligence (mesh), Workflow (mesh), Macular Degeneration (mesh), Female (mesh), Male (mesh), Aged (mesh), Eye Diseases (mesh), Reproducibility of Results (mesh), Middle Aged (mesh), 32 Biomedical and clinical sciences (for-2020), 42 Health sciences (for-2020)
Dateibeschreibung: application/pdf
-
6
Autoren: et al.
Quelle: International Angiology. 44(3)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3202 Clinical Sciences (for-2020), Stroke (rcdc), Prevention (rcdc), Machine Learning and Artificial Intelligence (rcdc), Neurosciences (rcdc), Cerebrovascular (rcdc), Brain Disorders (rcdc), Cardiovascular (hrcs-hc), 3 Good Health and Well Being (sdg), Humans (mesh), Carotid Stenosis (mesh), Asymptomatic Diseases (mesh), Stroke (mesh), Risk Assessment (mesh), Risk Factors (mesh), Machine Learning (mesh), Artificial Intelligence (mesh), Carotid stenosis, Stroke, Transient ischemic attack, Humans (mesh), Carotid Stenosis (mesh), Risk Assessment (mesh), Risk Factors (mesh), Artificial Intelligence (mesh), Stroke (mesh), Asymptomatic Diseases (mesh), Machine Learning (mesh), Humans (mesh), Carotid Stenosis (mesh), Asymptomatic Diseases (mesh), Stroke (mesh), Risk Assessment (mesh), Risk Factors (mesh), Machine Learning (mesh), Artificial Intelligence (mesh), 1103 Clinical Sciences (for), Cardiovascular System & Hematology (science-metrix), 3201 Cardiovascular medicine and haematology (for-2020), 3202 Clinical sciences (for-2020)
Dateibeschreibung: application/pdf
-
7
Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies
Autoren: et al.
Quelle: Cancer Letters. 612
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3211 Oncology and Carcinogenesis (for-2020), Bioengineering (rcdc), Cancer (rcdc), Machine Learning and Artificial Intelligence (rcdc), Precision Medicine (rcdc), Digestive Diseases (rcdc), Orphan Drug (rcdc), Prevention (rcdc), Genetics (rcdc), Rare Diseases (rcdc), Human Genome (rcdc), Cancer (hrcs-hc), 3 Good Health and Well Being (sdg), Humans (mesh), Gastrointestinal Neoplasms (mesh), Artificial Intelligence (mesh), Prognosis (mesh), Precision Medicine (mesh), Biomarkers, Tumor (mesh), Gastrointestinal cancer, Artificial intelligence, Diagnosis, Biomarkers, Therapy, Humans (mesh), Gastrointestinal Neoplasms (mesh), Prognosis (mesh), Artificial Intelligence (mesh), Biomarkers, Tumor (mesh), Precision Medicine (mesh), Artificial intelligence, Biomarkers, Diagnosis, Gastrointestinal cancer, Therapy, Humans (mesh), Gastrointestinal Neoplasms (mesh), Artificial Intelligence (mesh), Prognosis (mesh), Precision Medicine (mesh), Biomarkers, Tumor (mesh), 1112 Oncology and Carcinogenesis (for), Oncology & Carcinogenesis (science-metrix), 3211 Oncology and carcinogenesis (for-2020)
Zugangs-URL: https://escholarship.org/uc/item/2dr9j4kt
-
8
Autoren: et al.
Quelle: BMJ Open. 15(2)
Schlagwörter: 4206 Public Health (for-2020), 42 Health Sciences (for-2020), Machine Learning and Artificial Intelligence (rcdc), Diabetes (rcdc), Nutrition (rcdc), Data Science (rcdc), Clinical Research (rcdc), Behavioral and Social Science (rcdc), Bioengineering (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Metabolic and endocrine (hrcs-hc), 3 Good Health and Well Being (sdg), Humans (mesh), Cross-Sectional Studies (mesh), Diabetes Mellitus, Type 2 (mesh), Artificial Intelligence (mesh), Male (mesh), Female (mesh), Middle Aged (mesh), Adult (mesh), Research Design (mesh), Aged (mesh), Machine Learning (mesh), Diabetic nephropathy & vascular disease, Diabetic retinopathy, Diabetic neuropathy, Diabetes Mellitus, Type 2, EPIDEMIOLOGY, AI-READI Consortium, Humans (mesh), Diabetes Mellitus, Type 2 (mesh), Cross-Sectional Studies (mesh), Research Design (mesh), Artificial Intelligence (mesh), Adult (mesh), Aged (mesh), Middle Aged (mesh), Female (mesh), Male (mesh), Machine Learning (mesh), Diabetes Mellitus, Type 2, Diabetic nephropathy & vascular disease, Diabetic neuropathy, Diabetic retinopathy, EPIDEMIOLOGY, Humans (mesh), Cross-Sectional Studies (mesh), Diabetes Mellitus, Type 2 (mesh), Artificial Intelligence (mesh), Male (mesh), Female (mesh), Middle Aged (mesh), Adult (mesh), Research Design (mesh), Aged (mesh), Machine Learning (mesh), 1103 Clinical Sciences (for), 1117 Public Health and Health Services (for), 1199 Other Medical and Health Sciences (for), 32 Biomedical and clinical sciences (for-2020), 42 Health sciences (for-2020), 52 Psychology (for-2020)
Dateibeschreibung: application/pdf
-
9
Autoren: et al.
Quelle: JMIR Public Health and Surveillance. 11
Schlagwörter: 4206 Public Health (for-2020), 42 Health Sciences (for-2020), Minority Health (rcdc), Prevention (rcdc), American Indian or Alaska Native (rcdc), Clinical Research (rcdc), Humans (mesh), Artificial Intelligence (mesh), California (mesh), COVID-19 (mesh), COVID-19 Vaccines (mesh), Indians, North American (mesh), Patient Selection (mesh), Social Media (mesh), social media recruitment, indigenous populations, Native American populations, public health research, survey design, data quality, Humans (mesh), Patient Selection (mesh), Artificial Intelligence (mesh), Indians, North American (mesh), California (mesh), Social Media (mesh), COVID-19 (mesh), COVID-19 Vaccines (mesh), Native American populations, data quality, indigenous populations, public health research, social media recruitment, survey design, Humans (mesh), Artificial Intelligence (mesh), California (mesh), COVID-19 (mesh), COVID-19 Vaccines (mesh), Indians, North American (mesh), Patient Selection (mesh), Social Media (mesh), 4202 Epidemiology (for-2020), 4203 Health services and systems (for-2020)
Dateibeschreibung: application/pdf
-
10
Autoren: et al.
Quelle: Ophthalmology Glaucoma. 8(1)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3212 Ophthalmology and Optometry (for-2020), Clinical Research (rcdc), Machine Learning and Artificial Intelligence (rcdc), Neurosciences (rcdc), Aging (rcdc), Neurodegenerative (rcdc), Eye Disease and Disorders of Vision (rcdc), Data Science (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Bioengineering (rcdc), Generic health relevance (hrcs-hc), Eye (hrcs-hc), Humans (mesh), Glaucoma (mesh), Artificial Intelligence (mesh), Deep Learning (mesh), Machine Learning (mesh), Federated Learning (mesh), Humans (mesh), Glaucoma (mesh), Artificial Intelligence (mesh), Machine Learning (mesh), Deep Learning (mesh), Federated Learning (mesh), Artificial intelligence, Federated learning, Glaucoma, Privacy, Humans (mesh), Glaucoma (mesh), Artificial Intelligence (mesh), Deep Learning (mesh), Machine Learning (mesh), Federated Learning (mesh)
Dateibeschreibung: application/pdf
-
11
Autoren: et al.
Quelle: Journal of Biomedical Semantics. 15(1)
Schlagwörter: 4605 Data Management and Data Science (for-2020), 46 Information and Computing Sciences (for-2020), 4602 Artificial Intelligence (for-2020), Networking and Information Technology R&D (NITRD) (rcdc), Generic health relevance (hrcs-hc), Biological Ontologies (mesh), Artificial Intelligence (mesh), Natural Language Processing (mesh), Information Storage and Retrieval (mesh), Artificial Intelligence (mesh), Natural Language Processing (mesh), Information Storage and Retrieval (mesh), Biological Ontologies (mesh), Artificial intelligence, Biocuration, Knowledge graphs, Large language models, Ontologies, Ontology engineering, Biological Ontologies (mesh), Artificial Intelligence (mesh), Natural Language Processing (mesh), Information Storage and Retrieval (mesh), 0699 Other Biological Sciences (for), 0801 Artificial Intelligence and Image Processing (for), 0806 Information Systems (for), 46 Information and computing sciences (for-2020)
Dateibeschreibung: application/pdf
-
12
Autoren: et al.
Quelle: Nature Medicine. 30(10)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3202 Clinical Sciences (for-2020), Clinical Trials and Supportive Activities (rcdc), Clinical Research (rcdc), 6.1 Pharmaceuticals (hrcs-rac), Oral and gastrointestinal (hrcs-hc), Humans (mesh), Artificial Intelligence (mesh), Clinical Trials as Topic (mesh), Non-alcoholic Fatty Liver Disease (mesh), Liver Cirrhosis (mesh), Patient Selection (mesh), Endpoint Determination (mesh), Female (mesh), Retrospective Studies (mesh), Male (mesh), Automation (mesh), Liver Diseases (mesh), Reproducibility of Results (mesh), Humans (mesh), Liver Diseases (mesh), Liver Cirrhosis (mesh), Endpoint Determination (mesh), Retrospective Studies (mesh), Reproducibility of Results (mesh), Patient Selection (mesh), Automation (mesh), Artificial Intelligence (mesh), Female (mesh), Male (mesh), Clinical Trials as Topic (mesh), Non-alcoholic Fatty Liver Disease (mesh), Humans (mesh), Artificial Intelligence (mesh), Clinical Trials as Topic (mesh), Non-alcoholic Fatty Liver Disease (mesh), Liver Cirrhosis (mesh), Patient Selection (mesh), Endpoint Determination (mesh), Female (mesh), Retrospective Studies (mesh), Male (mesh), Automation (mesh), Liver Diseases (mesh), Reproducibility of Results (mesh), 11 Medical and Health Sciences (for), Immunology (science-metrix), 32 Biomedical and clinical sciences (for-2020), 42 Health sciences (for-2020)
Dateibeschreibung: application/pdf
-
13
Autoren: et al.
Quelle: Вестник медицинского института «Реавиз»: Реабилитация, врач и здоровье, Vol 15, Iss 1, Pp 22-29 (2025)
Schlagwörter: colitis, ulcerative [mesh id: d003093], Medicine (General), artificial intelligence [mesh id: d001185], R5-920, deep learning [mesh id: d000082062], colorectal neoplasms [mesh id: d015179], crohn disease [mesh id: d003424], inflammatory bowel diseases [mesh id: d015212], machine learning [mesh id: d065007], neural networks, computer [mesh id: d017209], endoscopy [mesh id: d004724], histopathology [mesh id: d006660]
-
14
Autoren: et al.
Quelle: Cancer Medicine. 13(12)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3211 Oncology and Carcinogenesis (for-2020), Bioengineering (rcdc), Cancer (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Machine Learning and Artificial Intelligence (rcdc), Clinical Research (rcdc), Cancer (hrcs-hc), Humans (mesh), Artificial Intelligence (mesh), Medical Oncology (mesh), Neoplasms (mesh), Humans (mesh), Neoplasms (mesh), Medical Oncology (mesh), Artificial Intelligence (mesh), Artificial Intelligence, Cancer Outcomes Research, Large language models, Observational Data, prognostic factor, Humans (mesh), Artificial Intelligence (mesh), Medical Oncology (mesh), Neoplasms (mesh), 0601 Biochemistry and Cell Biology (for), 1112 Oncology and Carcinogenesis (for), 3211 Oncology and carcinogenesis (for-2020)
Dateibeschreibung: application/pdf
-
15
Autoren: et al.
Quelle: Journal of Cardiovascular Computed Tomography. 18(4)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3201 Cardiovascular Medicine and Haematology (for-2020), 3202 Clinical Sciences (for-2020), Cardiovascular (rcdc), Aging (rcdc), Atherosclerosis (rcdc), Prevention (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Heart Disease (rcdc), Machine Learning and Artificial Intelligence (rcdc), Heart Disease - Coronary Heart Disease (rcdc), Cardiovascular (hrcs-hc), Humans (mesh), Female (mesh), Male (mesh), Peptide Fragments (mesh), Natriuretic Peptide, Brain (mesh), Aged (mesh), Heart Failure (mesh), Predictive Value of Tests (mesh), Coronary Artery Disease (mesh), Middle Aged (mesh), Risk Factors (mesh), Biomarkers (mesh), Vascular Calcification (mesh), Risk Assessment (mesh), Prognosis (mesh), United States (mesh), Time Factors (mesh), Incidence (mesh), Aged, 80 and over (mesh), Computed Tomography Angiography (mesh), Artificial Intelligence (mesh), Coronary Angiography (mesh), Radiographic Image Interpretation, Computer-Assisted (mesh), Reproducibility of Results (mesh), Multidetector Computed Tomography (mesh), Asymptomatic Diseases (mesh), Left ventricular volume, Coronary artery calcium, Artificial intelligence, Heart failure, NT-proBNP, Humans (mesh), Natriuretic Peptide, Brain (mesh), Peptide Fragments (mesh), Radiographic Image Interpretation, Computer-Assisted (mesh), Coronary Angiography (mesh), Prognosis (mesh), Incidence (mesh), Risk Assessment (mesh), Risk Factors (mesh), Reproducibility of Results (mesh), Predictive Value of Tests (mesh), Time Factors (mesh), Artificial Intelligence (mesh), Aged (mesh), Aged, 80 and over (mesh), Middle Aged (mesh), United States (mesh), Female (mesh), Male (mesh), Coronary Artery Disease (mesh), Heart Failure (mesh), Asymptomatic Diseases (mesh), Multidetector Computed Tomography (mesh), Vascular Calcification (mesh), Biomarkers (mesh), Computed Tomography Angiography (mesh), Artificial intelligence, Coronary artery calcium, Heart failure, Left ventricular volume, NT-proBNP, Humans (mesh), Female (mesh), Male (mesh), Peptide Fragments (mesh), Natriuretic Peptide, Brain (mesh), Aged (mesh), Heart Failure (mesh), Predictive Value of Tests (mesh), Coronary Artery Disease (mesh), Middle Aged (mesh), Risk Factors (mesh), Biomarkers (mesh), Vascular Calcification (mesh), Risk Assessment (mesh), Prognosis (mesh), United States (mesh), Time Factors (mesh), Incidence (mesh), Aged, 80 and over (mesh), Computed Tomography Angiography (mesh), Artificial Intelligence (mesh), Coronary Angiography (mesh), Radiographic Image Interpretation, Computer-Assisted (mesh), Reproducibility of Results (mesh), Multidetector Computed Tomography (mesh), Asymptomatic Diseases (mesh), 1102 Cardiorespiratory Medicine and Haematology (for), 1103 Clinical Sciences (for), Cardiovascular System & Hematology (science-metrix), 3201 Cardiovascular medicine and haematology (for-2020), 3202 Clinical sciences (for-2020), 4601 Applied computing (for-2020)
Dateibeschreibung: application/pdf
-
16
Autoren: et al.
Quelle: Eye. 38(6)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3212 Ophthalmology and Optometry (for-2020), Machine Learning and Artificial Intelligence (rcdc), Eye Disease and Disorders of Vision (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Bioengineering (rcdc), Neurosciences (rcdc), Humans (mesh), Artificial Intelligence (mesh), Retina (mesh), Retinal Vessels (mesh), Algorithms (mesh), Neural Networks, Computer (mesh), Retinal Vessels (mesh), Retina (mesh), Humans (mesh), Algorithms (mesh), Artificial Intelligence (mesh), Neural Networks, Computer (mesh), Humans (mesh), Artificial Intelligence (mesh), Retina (mesh), Retinal Vessels (mesh), Algorithms (mesh), Neural Networks, Computer (mesh), 1103 Clinical Sciences (for), 1107 Immunology (for), 1113 Opthalmology and Optometry (for), Ophthalmology & Optometry (science-metrix), 3204 Immunology (for-2020), 3212 Ophthalmology and optometry (for-2020)
Dateibeschreibung: application/pdf
-
17
Autoren: et al.
Quelle: Retina. 44(3)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3212 Ophthalmology and Optometry (for-2020), Bioengineering (rcdc), Clinical Research (rcdc), Aging (rcdc), Machine Learning and Artificial Intelligence (rcdc), Macular Degeneration (rcdc), Neurodegenerative (rcdc), Biomedical Imaging (rcdc), Eye Disease and Disorders of Vision (rcdc), Neurosciences (rcdc), 4.1 Discovery and preclinical testing of markers and technologies (hrcs-rac), Eye (hrcs-hc), Humans (mesh), Artificial Intelligence (mesh), Tomography, Optical Coherence (mesh), Retrospective Studies (mesh), Cross-Sectional Studies (mesh), Fluorescein Angiography (mesh), Choroidal Neovascularization (mesh), Wet Macular Degeneration (mesh), Geographic Atrophy (mesh), age-related macular degeneration, artificial intelligence, classification prediction, CNV, deep learning, image analysis, OCTA, Humans (mesh), Choroidal Neovascularization (mesh), Tomography, Optical Coherence (mesh), Fluorescein Angiography (mesh), Retrospective Studies (mesh), Cross-Sectional Studies (mesh), Artificial Intelligence (mesh), Geographic Atrophy (mesh), Wet Macular Degeneration (mesh), Humans (mesh), Artificial Intelligence (mesh), Tomography, Optical Coherence (mesh), Retrospective Studies (mesh), Cross-Sectional Studies (mesh), Fluorescein Angiography (mesh), Choroidal Neovascularization (mesh), Wet Macular Degeneration (mesh), Geographic Atrophy (mesh), 1113 Opthalmology and Optometry (for), Ophthalmology & Optometry (science-metrix), 3212 Ophthalmology and optometry (for-2020)
Dateibeschreibung: application/pdf
-
18
Autoren: et al.
Quelle: Journal of the American College of Radiology. 21(2)
Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3211 Oncology and Carcinogenesis (for-2020), Clinical Research (rcdc), Prevention (rcdc), Women's Health (rcdc), Cancer (rcdc), Machine Learning and Artificial Intelligence (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Bioengineering (rcdc), Breast Cancer (rcdc), Biomedical Imaging (rcdc), Cancer (hrcs-hc), 3 Good Health and Well Being (sdg), Mammography (mesh), Humans (mesh), Breast Neoplasms (mesh), Female (mesh), Artificial Intelligence (mesh), Risk Assessment (mesh), Early Detection of Cancer (mesh), Risk Factors (mesh), Predictive Value of Tests (mesh), Algorithms (mesh), Artificial intelligence, risk prediction, screening mammography, Humans (mesh), Breast Neoplasms (mesh), Mammography (mesh), Risk Assessment (mesh), Risk Factors (mesh), Predictive Value of Tests (mesh), Algorithms (mesh), Artificial Intelligence (mesh), Female (mesh), Early Detection of Cancer (mesh), Artificial intelligence, risk prediction, screening mammography, Mammography (mesh), Humans (mesh), Breast Neoplasms (mesh), Female (mesh), Artificial Intelligence (mesh), Risk Assessment (mesh), Early Detection of Cancer (mesh), Risk Factors (mesh), Predictive Value of Tests (mesh), Algorithms (mesh), 1103 Clinical Sciences (for), 1117 Public Health and Health Services (for), Nuclear Medicine & Medical Imaging (science-metrix), 3202 Clinical sciences (for-2020)
Dateibeschreibung: application/pdf
-
19
Autoren: et al.
Quelle: Nature Communications. 15(1)
Schlagwörter: 4613 Theory Of Computation (for-2020), 3404 Medicinal and Biomolecular Chemistry (for-2020), 34 Chemical Sciences (for-2020), 33 Built Environment and Design (for-2020), 46 Information and Computing Sciences (for-2020), 3303 Design (for-2020), Machine Learning and Artificial Intelligence (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), 5.1 Pharmaceuticals (hrcs-rac), Cancer (hrcs-hc), Drug Discovery (mesh), Humans (mesh), Molecular Docking Simulation (mesh), Artificial Intelligence (mesh), NAV1.7 Voltage-Gated Sodium Channel (mesh), Protein Binding (mesh), Crystallography, X-Ray (mesh), Ligands (mesh), Ubiquitin-Protein Ligases (mesh), Small Molecule Libraries (mesh), Drug Evaluation, Preclinical (mesh), Humans (mesh), Ubiquitin-Protein Ligases (mesh), Ligands (mesh), Crystallography, X-Ray (mesh), Drug Evaluation, Preclinical (mesh), Protein Binding (mesh), Artificial Intelligence (mesh), Small Molecule Libraries (mesh), Drug Discovery (mesh), NAV1.7 Voltage-Gated Sodium Channel (mesh), Molecular Docking Simulation (mesh), Drug Discovery (mesh), Humans (mesh), Molecular Docking Simulation (mesh), Artificial Intelligence (mesh), NAV1.7 Voltage-Gated Sodium Channel (mesh), Protein Binding (mesh), Crystallography, X-Ray (mesh), Ligands (mesh), Ubiquitin-Protein Ligases (mesh), Small Molecule Libraries (mesh), Drug Evaluation, Preclinical (mesh)
Dateibeschreibung: application/pdf
-
20
Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time
Autoren: et al.
Quelle: Neuroradiology
Schlagwörter: Male, Adult, Artificial intelligence, LESIONS, Multiple Sclerosis, Time Factors, SEGMENTATION, Brain, Automated assessment, Middle Aged, DIAGNOSIS, Magnetic Resonance Imaging, Multiple Sclerosis/diagnostic imaging [MeSH], Female [MeSH], Brain/diagnostic imaging [MeSH], Adult [MeSH], Humans [MeSH], Middle Aged [MeSH], MRI, AI, Time Factors [MeSH], Artificial Intelligence [MeSH], Multiple sclerosis, Male [MeSH], Image Interpretation, Computer-Assisted/methods [MeSH], Magnetic resonance imaging, Magnetic Resonance Imaging/methods [MeSH], Diagnostic Neuroradiology, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Image Interpretation, Computer-Assisted, Humans, Female
Nájsť tento článok vo Web of Science 
Full Text Finder