All traces

ML applications in clinical microbiology face typical challenges including class imbalance, limited generalization, robustness issues, and need for model interpretation and explainability to achieve robust performance under real-world variability.

Models that were near-perfect during internal cross-validation diverged significantly on the separate external cohort, showing reduced out-of-distribution generalization for cross-species histological classification.

ML, DL and RL-based cybersecurity solutions are susceptible to adversarial attacks, and ChatGPT-like tools can be manipulated to threaten data integrity, confidentiality and availability.

The LLM-assisted workflow did not consistently outperform ICD-based retrieval, with no statistically significant AUC difference in Cohort 1 and lower AUC than ICD codes for heart failure in that cohort.

The model missed 21.2% of expert-defined high-risk cases with sensitivity of 78.8%, and three-level risk stratification accuracy was only 76.3% with 98.6% of errors being downward shifts that underestimate risk.

Most AI models for predicting laparoscopic cholecystectomy difficulty carry high risk of bias, rarely undergo external validation, and have significant methodological flaws limiting clinical translation.

When tuned for ≥95% sensitivity screening, the models suffered a steep parallel decline in specificity and cannot safely eliminate the need for lumbar puncture, with top non-linear models showing severe training optimism.

Labor-intensive segmentation and factors like comminution, bilateral injury, loss of anatomic references, and metal artifacts still restrict efficiency and reproducibility of digital reconstruction workflows.

Because the dataset was split at the image level, correlated frames from the same examination may inflate performance, so results cannot be interpreted as patient-level generalization and require grouped reanalysis and external validation before clinical use.

Removing the visible watermark makes AI-generated images, videos and music less immediately identifiable as synthetic to casual viewers, raising risk of audience confusion about authenticity.

Most AI-ECG studies in pediatric and congenital heart disease remain retrospective and single-center with limited external validation, facing challenges from small datasets and age-dependent ECG variation.

Stakeholders flag unresolved risks for AI in the medicine lifecycle around accuracy and reliability, data governance confidentiality and consent, and ethics fairness and bias prevention requiring further regulatory science research.

AI-enabled medical devices create dynamic, data-dependent hazards that current ISO 14971, AAMI CR34971 and EU AI Act approaches address only in fragmented form, leaving gaps in hazard linkage, control adequacy, and lifecycle monitoring.

Prompt-engineered simplification performed significantly worse for completeness and harmfulness in specific diagnoses and provided generic education disconnected from pathological findings.

Use of generative AI in healthcare introduces challenges including lack of professional expertise in decision making, risk to patient data privacy, difficulty integrating with existing healthcare systems, and data bias.