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Apparent superiority of ML over logistic regression for trauma mortality prediction may be inflated by convergent practices including comparing best-of-several ML models to a single LR comparator, reliance on internal validation, selective reporting, and AUC-only synthesis.

Deep learning models for MCI-to-AD conversion face substantial barriers to routine clinical use due to heavy reliance on ADNI, lack of diverse multicenter data, overfitting, and poor interpretability.

Critics argue AB 1709 functions as a ban for under-16s that cuts young people off from digital lifelines and speech without making them safer or healthier.

Digital devices, social media and AI tools influence brain function and cognitive abilities, with potential negative impacts on attention, memory and related functions.

Models were validated only at sample level without patient or centre grouping, and exploratory risk strata were not evaluated for clinical utility or safety, so they do not establish symptomatic UTI or safe antibiotic decisions.

Half of included studies had identified risk of bias and clinical adoption remains limited, requiring multicenter prospective validation and interpretable frameworks before bedside use.

Visual HRCT scoring remains reader-dependent and AI outputs lack prospective multicenter validation and protocol harmonization needed to serve as treatment-triggering biomarkers.

Chatbots sometimes confused similar congenital anomalies and provided less detailed anatomical descriptions in complex tumor cases, requiring caution and verification by qualified professionals before clinical use.

Participants identified trust as the central barrier to using an AI companion, driven by privacy concerns and vulnerabilities specific to schizophrenia.

Students with medium and low baseline history-taking proficiency showed relatively limited score improvements from LLM-VSP self-practice, with practice frequency alone not independently predicting final performance.

Most hand surgery AI tools are deployed in unaudited workflows and rarely remeasured after release after testing only on training-like data, leaving the hand surgeon accountable for patient outcomes shaped by opaque models.

AI tools translating radiology reports into plain language can produce translation errors that compromise comprehension and safety, causing reports to be graded unsafe and warrant withholding from patients.

In medical education, the same AI tools may reduce training safety by quietly reshaping how future clinicians think and act, functioning as a wedge that undermines training.

Machine learning models for autism spectrum disorder prediction that use data augmentation and feature selection have limited external validation and inadequate evaluation frameworks, reducing confidence in reported performance improvements and model generalizability.

AI-based assessment introduces distinct validity threats across scoring, generalisation, extrapolation and implications, including contamination, instability, inequities, automation bias and deskilling when used for consequential learner progression decisions.