The increasing availability of biomedical data from large biobanks, electronic health records,
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …

Importance Despite the potential of machine learning to improve multiple aspects of patient
care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a …

The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such
algorithms may be shaped by various factors such as social determinants of health that can …

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment
outcomes including efficacy and toxicity, thereby supporting the assessment and safety of …

Artificial intelligence systems are increasingly being applied to healthcare. In surgery, AI
applications hold promise as tools to predict surgical outcomes, assess technical skills, or …

Algorithmic fairness (AF) has been framed as a newly emerging technology that mitigates
systemic discrimination in automated decision‐making, providing opportunities to improve …

Various forms of artificial intelligence (AI) applications are being deployed and used in many
healthcare systems. As the use of these applications increases, we are learning the failures …

With the rise of artificial intelligence (AI), the issue of trust in AI emerges as a paramount
societal concern. Despite increased attention of researchers, the topic remains fragmented …

The promise of highly personalized oncology care using artificial intelligence (AI)
technologies has been forecasted since the emergence of the field. Cumulative advances …

Objectives Federated learning (FL) allows multiple institutions to collaboratively develop a
machine learning algorithm without sharing their data. Organizations instead share model …