Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis.
Song Da-Yea et al. Neuroradiology 2021

Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing.
Long Erping et al. NPJ digital medicine 2021 3(1) 112

Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation.
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Risk stratification of early-stage cervical cancer with intermediate-risk factor: model development and validation based on machine learning algorithm.
Chu Ran et al. The oncologist 2021

A Pipeline for Predicting the Treatment Response of Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer Using Single MRI Modality: Combining Deep Segmentation Network and Radiomics Analysis Based on "Suspicious Region".
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Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer.
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Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.
Liu Shunli et al. European radiology 2021

Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning.
Liang Fukui et al. Journal of healthcare engineering 2021 20219971325

Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes.
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Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma.
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Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data.
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Uncovering barriers to screening for distress in patients with cancer via machine learning.
Günther Moritz Philipp et al. Journal of the Academy of Consultation-Liaison Psychiatry 2021

Evaluation of novel LCI CAD EYE system for real time detection of colon polyps.
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Machine learning applications in tobacco research: a scoping review.
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Dietary Intake and Health Status of Elderly Patients With Type 2 Diabetes Mellitus: Cross-sectional Study Using a Mobile App in Primary Care.
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Detecting diabetic retinopathy through machine learning on electronic health record data from an urban, safety net healthcare system.
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Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register.
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Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals.
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A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis.
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Predicting 1-Year Mortality after Hip Fracture Surgery: An Evaluation of Multiple Machine Learning Approaches.
Forssten Maximilian Peter et al. Journal of personalized medicine 2021 11(8)

Deep Learning Algorithm for Management of Diabetes Mellitus via Electrocardiogram-Based Glycated Hemoglobin (ECG-HbA1c): A Retrospective Cohort Study.
Lin Chin-Sheng et al. Journal of personalized medicine 2021 11(8)

Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations.
Prakash Jayant et al. Brain sciences 2021 11(8)

Digital Natives' Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study.
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Machine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings.
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Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis.
Qin Kaiwen et al. Surgical endoscopy 2021

Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare.
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A survey of extant organizational and computational setups for deploying predictive models in health systems.
Kashyap Sehj et al. Journal of the American Medical Informatics Association : JAMIA 2021

Artificial intelligence in the management and treatment of burns: a systematic review.
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Artificial Intelligence: Guidance for clinical imaging and therapeutic radiography professionals, a summary by the Society of Radiographers AI working group.
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A systematic review of machine learning and automation in burn wound evaluation: A promising but developing frontier.
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Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions.
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Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers.
Xie Wenxiu et al. International journal of environmental research and public health 2021 18(16)

Psychiatry in the Digital Age: A Blessing or a Curse?
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Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling.
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Current Status and Future Perspective of Artificial Intelligence in the Management of Peptic Ulcer Bleeding: A Review of Recent Literature.
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A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work.
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Radiomics: a primer on high-throughput image phenotyping.
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Recommendations for Integrating the Fundamentals of Machine Learning Into Medical Curricula.
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Application of artificial intelligence in gastrointestinal disease: a narrative review.
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Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review.
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Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study.
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Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records.
Somani Sulaiman et al. JAMIA open 2021 4(3) ooab068

The Role of Deep Learning-Based Echocardiography in the Diagnosis and Evaluation of the Effects of Routine Anti-Heart-Failure Western Medicines in Elderly Patients with Acute Left Heart Failure.
Chen Jinyou et al. Journal of healthcare engineering 2021 20214845792

Characterizing Mitral Regurgitation With Precision Phenotyping and Unsupervised Learning.
Ouyang David et al. JACC. Cardiovascular imaging 2021

A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations.
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A proof of concept study for machine learning application to stenosis detection.
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Validation of a Whole Heart Segmentation from Computed Tomography Imaging Using a Deep-Learning Approach.
Sharobeem Sam et al. Journal of cardiovascular translational research 2021

Machine learning model to predict hypotension after starting continuous renal replacement therapy.
Kang Min Woo et al. Scientific reports 2021 11(1) 17169

Deep neural network-estimated electrocardiographic age as a mortality predictor.
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Prediction of vaccine hesitancy based on social media traffic among Israeli parents using machine learning strategies.
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Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms.
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A Promising Approach: Artificial Intelligence Applied to Small Intestinal Bacterial Overgrowth (SIBO) Diagnosis Using Cluster Analysis.
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Utility of a Short Neuropsychological Protocol for Detecting HIV-Associated Neurocognitive Disorders in Patients with Asymptomatic HIV-1 Infection.
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