Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities

Towards an interoperable healthcare information infrastructure - working from the bottom up

Historically, the healthcare system has not made effective use of information technology. On the face of things, it would seem to provide a natural and richly varied domain in which to target benefit from IT solutions. But history shows that it is one of the most difficult domains in which to bring them to fruition. This paper provides an overview of the changing context and information requirements of healthcare that help to explain these characteristics.First and foremost, the disciplines and professions that healthcare encompasses have immense complexity and diversity to deal with, in structuring knowledge about what medicine and healthcare are, how they function, and what differentiates good practice and good performance. The need to maintain macro-economic stability of the health service, faced with this and many other uncertainties, means that management bottom lines predominate over choices and decisions that have to be made within everyday individual patient services. Individual practice and care, the bedrock of healthcare, is, for this and other reasons, more and more subject to professional and managerial control and regulation.One characteristic of organisations shown to be good at making effective use of IT is their capacity to devolve decisions within the organisation to where they can be best made, for the purpose of meeting their customers' needs. IT should, in this context, contribute as an enabler and not as an enforcer of good information services. The information infrastructure must work effectively, both top down and bottom up, to accommodate these countervailing pressures. This issue is explored in the context of infrastructure to support electronic health records.Because of the diverse and changing requirements of the huge healthcare sector, and the need to sustain health records over many decades, standardised systems must concentrate on doing the easier things well and as simply as possible, while accommodating immense diversity of requirements and practice. The manner in which the healthcare information infrastructure can be formulated and implemented to meet useful practical goals is explored, in the context of two case studies of research in CHIME at UCL and their user communities.Healthcare has severe problems both as a provider of information and as a purchaser of information systems. This has an impact on both its customer and its supplier relationships. Healthcare needs to become a better purchaser, more aware and realistic about what technology can and cannot do and where research is needed. Industry needs a greater awareness of the complexity of the healthcare domain, and the subtle ways in which information is part of the basic contract between healthcare professionals and patients, and the trust and understanding that must exist between them. It is an ideal domain for deeper collaboration between academic institutions and industry

Privacy-preserving machine learning for healthcare: open challenges and future perspectives

Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.Comment: ICLR 2023 Workshop on Trustworthy Machine Learning for Healthcare (TML4H

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in each domain is driven by a growing body of annotated data, increased computational resources, and technological innovations. In medicine, the sensitivity of the data, the complexity of the tasks, the potentially high stakes, and a requirement of accountability give rise to a particular set of challenges. In this review, we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making. (1) Explainable AI aims to produce a human-interpretable justification for each output. Such models increase confidence if the results appear plausible and match the clinicians expectations. However, the absence of a plausible explanation does not imply an inaccurate model. Especially in highly non-linear, complex models that are tuned to maximize accuracy, such interpretable representations only reflect a small portion of the justification. (2) Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains. For example, a classification task based on images acquired on different acquisition hardware. (3) Federated learning enables learning large-scale models without exposing sensitive personal health information. Unlike centralized AI learning, where the centralized learning machine has access to the entire training data, the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates, not personal health data. This narrative review covers the basic concepts, highlights relevant corner-stone and state-of-the-art research in the field, and discusses perspectives.Comment: This paper is accepted in IEEE CAA Journal of Automatica Sinica, Nov. 10 202