Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?

Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning

“…It is well known that a key challenge to implementing AI in clinical decision-making is the fairness of the underlying large clinical datasets. 119 These biases can lead to inherently flawed models. Similar challenges pertain to multi-omics integration of cellular and molecular datasets using machine learning methods.…”

Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review

“…Finally, important ethical concerns exist as regards the potential risk for new technologies to exacerbate existing disparities in healthcare as a result of social, economic, racial, or ethnic characteristics [ 4 , 39 , 40 ]. As healthcare has shifted to increasingly rely on digital tools for patient care, digital inclusion has become critical to promote healthcare equity; the emerging digital ecosystems have to be re-designed according to proactive engagement, planning, and implementation [ 39 , 41 , 42 ].…”

A Process Framework for Ethically Deploying Artificial Intelligence in Oncology

“…17,19,20 Indeed, models built on biased data cannot predict accurately, 5,21 but an environment of hype will engender such models' use. 22 Alternatively, if the oncology community develops standards and processes for AI's ethical development and monitoring, it can help remediate care inequities rather than extend them. 5,17,23 The ability to create cancer care AI that minimizes bias while maximizing privacy and autonomy rests upon whose data are intentionally included or excluded, when patients consent to data use, and when that use is disclosed.…”