[HTML][HTML] Trust and acceptability of data-driven clinical recommendations in everyday practice: A sco** review
Background Increasing attention is being given to the analysis of large health datasets to
derive new clinical decision support systems (CDSS). However, few data-driven CDSS are …
derive new clinical decision support systems (CDSS). However, few data-driven CDSS are …
Disability 4.0: bioethical considerations on the use of embodied artificial intelligence
Robotics and artificial intelligence have marked the beginning of a new era in the care and
integration of people with disabilities, hel** to promote their independence, autonomy and …
integration of people with disabilities, hel** to promote their independence, autonomy and …
Robotics and AI into healthcare from the perspective of European regulation: who is responsible for medical malpractice?
The integration of robotics and artificial intelligence into medical practice is radically
revolutionising patient care. This fusion of advanced technologies with healthcare offers a …
revolutionising patient care. This fusion of advanced technologies with healthcare offers a …
Integrating the patient voice: patient-centred and equitable clinical risk prediction for kidney health and disease
Applying a person-centred lens has implications for several aspects of risk prediction
research. Incorporating the patient voice may involve partnering with patients as researchers …
research. Incorporating the patient voice may involve partnering with patients as researchers …
Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives
AA Verma, P Trbovich, M Mamdani… - BMJ Quality & …, 2024 - qualitysafety.bmj.com
Machine learning (ML) solutions are increasingly entering healthcare. They are complex,
sociotechnical systems that include data inputs, ML models, technical infrastructure and …
sociotechnical systems that include data inputs, ML models, technical infrastructure and …