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AI applications in healthcare could expand access and improve disease surveillance and health system planning for marginalised populations in Sub-Saharan Africa.

AI software-as-a-medical-device platforms cleared since 2019 that estimate sleep parameters improve accessibility to obstructive sleep apnea diagnosis for patients unable or unwilling to undergo in-laboratory polysomnography.

A random forest model trained on 16S rRNA microbial community data from a field mesocosm predicted antifouling paint particle presence in sediment, with perfect detection of presence in test set and correct classification of all uncontaminated and 3 of 5 contaminated real-world Baltic Sea sites.

AI models for CT, MRI, and ultrasound diagnosis of urological cancers achieved higher pooled specificity and AUC than clinicians in a 110-study meta-analysis.

Deep learning models demonstrated proof-of-concept ability to predict knee osteoarthritis progression from medical imaging, with internal median AUCs up to 0.87 for surgical endpoints.

In 298 Iranian male taxi drivers, ROC and Random Forest analysis of the Persian CAARS-S:SV identified total-score cutoffs with 88% sensitivity and 86.7% specificity for adult ADHD screening.

An exploratory Light Gradient Boosting Machine model predicted 1-week willingness to reuse rubber dam isolation after microscopic root canal treatment with AUC 0.939 in held-out test and 0.983 in temporal validation.

AI research is moving ADHD diagnosis away from subjective interviews toward objective, data-driven tools, with EEG emerging as preferred modality for recent models.

Federated learning models trained on distributed patient data improved predictive performance over single-site local models across AUC, F1, sensitivity, PPV and PRAUC.

AI-assisted voter verification, biometric identification, and result-monitoring systems improved administrative coordination and voter-list accuracy in elections in Thailand, Indonesia, Philippines and Myanmar.

Researchers propose an HDR governance framework for AI-created human digital twins that would protect citizen autonomy after death through advance data directives and data trustees.

Efficient low-power field target detection, measured as shorter fixation duration and faster time to first target fixation, predicts higher diagnostic accuracy in digital cytology and can be rapidly acquired through standard 3-month training.

AI systems can provide simple, clear, well-documented answers to clinical questions about managing patients with hypertension to assist practicing physicians.

GPT-4 generated responses to 20 psychosis psychoeducational questions that were rated highly for accuracy, clarity, completeness and clinical utility.

Generative AI can personalize radiology trainee learning pathways and generate synthetic imaging cases and board-style questions to augment curriculum planning and assessment.