Titlebook: AI for Health Equity and Fairness; Leveraging AI to Add Arash Shaban-Nejad,Martin Michalowski,Simone Bianc Book 2024 The Editor(s) (if appl

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cartilage

Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories, Warning Scores (EWS) are widely deployed to measure overall health status, and risk of adverse outcomes, in hospital patients. However, current EWS are limited both by their lack of personalisation and use of static observations. We propose a pipeline that groups intensive care unit patients by the

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,Generation of Clinical Skin Images with Pathology with Scarce Data,ovided to healthcare providers and doctors. Dermatology is among the areas which can benefit from data-driven models, as the first step of identifying skin diseases typically consists of visual inspection (possibly followed by further analyses) and AI approaches are well-suited to classify images—if

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,MILFORMER: Weighted Dual Stream Class Centered Random Attention Multiple Instance Learning for Wholmerged as a pivotal strategy to address the scarcity of localized annotations in WSI analysis. However, in the current landscape of state-of-the-art methods, the instance-level accuracy of these models significantly lags behind that of the bag-level. This article introduces MILFormer, a novel multi-

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,DOST—Domain Obedient Self-supervision for Trustworthy Multi Label Classification with Noisy Labels,ystems. Deep learning systems rely on enormous amounts of data, often accompanied by annotation errors, and do not natively abide by well-known medical principles. In diagnostic scenarios, lack of adherence to domain constraints make systems unreliable, and this problem is only exacerbated by annota