Machine learning for healthcare (MLHC) is at the juncture of leaping from the pages of journals and conference proceedings to clinical implementation at the bedside. Succeeding in this endeavour requires the synthesis of insights from both the machine learning and healthcare domains, in order to ...
Equity in essence: A call for operationalising fairness in machine learning for healthcare Machine learning for healthcare (MLHC) is at the juncture of leaping from the pages of journals and conference proceedings to clinical implementation at th... JW Gichoya,LG Mccoy,LAG Celi,... - 《Bmj...
In Machine Learning for Healthcare Conf. 587–602 (PMLR, 2018). Prosper, A. E. et al. Association of inclusion of more black individuals in lung cancer screening with reduced mortality. JAMA Netw. Open 4, e2119629 (2021). Article PubMed PubMed Central Google Scholar National Lung ...
Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of tasks and challenges related to health; there is greater potential for impact by leveraging machine learning in health tasks more broa...
NatarajanGoogle ResearchAlan KarthikesalingamGoogle ResearchKatherine HellerGoogle ResearchSilvia ChiappaDeepMindAlexander D’AmourGoogle ResearchAbstractDiagnosing and mitigating changes in model fairness under distribution shift is animportant component of the safe deployment of machine learning in healthcare...
Potentialuse cases for AI in healthcarecontinue to grow as the technology rapidly advances. However, the potential for AI to enhance clinical decision support, chronic disease management and population health efforts has been checked by concerns over pitfalls like model bias and fairness...
具体的解释各位读者可以去阅读一些经典的machine learning paper。 1 先来看看摘要以及框架图: Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment. ...
the focus on implementing fairness is similar in English and Chinese literature. Scholars such as Jinping Zhang and Dan Lin highlight the international emphasis on fairness through mathematical and statistical methods, and analyzing accessibility and fairness of healthcare services for different populations...
development in recent years, showcasing its efficacy in diverse practical applications such as autonomous driving, recommendation systems, and more. Notably, the data-centric approach inherent in AI methodologies has emerged as an indispensable asset in the domains of healthcare and medical image ...
Artificial Intelligence (AI) has emerged as a transformative force in the financial sector, particularly in advancing financial inclusion. AI-driven technologies, including machine learning algorithms and automated decision-making systems, have expanded the reach of financial services to previously underserved...