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This review synthesizes recent evidence on machine learning models integrating multimodal data for relapse prediction in MDD, highlighting moderate accuracy driven mainly by clinical risk factors and advocating for dynamic approaches to bet
A deep learning model identifying ARDS features on chest X-rays independently predicts 28-day mortality and ventilator duration beyond traditional clinical measures in patients undergoing invasive mechanical ventilation.
A new machine learning model predicts post-rehabilitation walking ability in nontraumatic spinal cord injury patients with high accuracy, aiding early clinical decision-making and rehabilitation planning.
A multicenter study demonstrates that interpretable machine learning applied to baseline OCT biomarkers accurately predicts chronicity in central serous chorioretinopathy, facilitating early risk stratification and personalized management.
A no-code, agentic AI workflow successfully developed and externally validated a calibrated thyroid nodule malignancy classifier, demonstrating strong diagnostic performance across multiple cancer histologies and enhancing accessibility in
An explainable machine learning model identifies bariatric surgery candidates at risk for obesity hypoventilation syndrome using seven accessible clinical variables, enhancing early diagnosis and perioperative risk management.
This article reviews a validated machine learning decision-tree model using LC-MS/MS steroid profiles to accurately and rapidly diagnose congenital disorders of adrenal steroidogenesis (CDAS), enhancing clinical decision-making and patient
This study demonstrates that combining quantitative CT biomarkers with clinical data significantly improves the detection of previously undiagnosed COPD within lung cancer screening populations, optimizing referrals for confirmatory spirome
A multicenter study developed a robust exosomal microRNA-based model enabling preoperative detection of occult liver micrometastasis in pancreatic ductal adenocarcinoma, improving risk stratification and guiding treatment sequencing.
A novel AI-driven risk prediction model for ovarian cancer demonstrates high accuracy and potential to improve early detection in screening settings.
This article discusses a novel machine learning-based tool, especially XGBoost, to predict hearing loss risk in high-risk neonates using clinical factors for targeted early intervention.
Weekly well-day capillary ketone testing maintains predictive accuracy for 1-month diabetic ketoacidosis risk, offering a practical and less burdensome monitoring strategy for patients with type 1 diabetes.
A novel machine learning-based model demonstrates improved risk prediction in transthyretin amyloid cardiomyopathy, surpassing traditional staging systems and supporting personalized patient management.
This study introduces a validated machine learning tool providing personalized probability scores to improve cochlear implant candidacy screening, surpassing traditional rules by enabling tailored patient counseling and identifying borderli
This study delineates three unique molecular subtypes of cholangiocarcinoma via integrated multiomic and machine learning methodologies, highlighting TNK1 kinase as a promising targeted therapy in a metabolic subtype.
The PREVUE-VALVE Study demonstrates that AI-ECG models developed in hospital settings show reduced diagnostic accuracy when applied to community populations with lower disease prevalence and milder structural heart disease, underscoring the
Machine learning models can effectively predict major 30-day complications following glossectomy for tongue cancer, improving individualized risk assessment over traditional methods.
A machine learning model using simple clinical tests within 72 hours poststroke accurately predicts 6-month upper-limb motor outcomes, aiding rehabilitation planning.
Advanced MRI connectome analysis identifies disruption of key brain hubs as biomarkers predicting long-term seizure recurrence after temporal lobe epilepsy surgery, improving postoperative risk stratification.
Readmissions following chronic/subacute subdural hematoma hospitalizations are common and mostly nonsurgical, with varied clinical trajectories. Machine learning analysis reveals distinct patient clusters with unique readmission risks, unde
