Machine Learning in Healthcare

Dear Colleagues,

Research and development in machine learning (ML), and particular sub-fields such as deep learning (DL) and natural language processing (NLP), have enjoyed tremendous advances in performance over the past decade, thanks in part to hardware improvements such as GPUs and troves of labeled data available for benchmark. This general trend applies to healthcare and clinical care as well, where ML efforts in the field have been catalyzed by the volume and variety of data generated by maturing technologies such as electronic health record systems; high-throughput “-omics” methods; and the expanded usage of medical, wearable, and mobile devices. The application of these methods can range from hypothesis generation to decision support to fully automated AI systems, each associated with a unique set of requirements and challenges that must be met in order to meaningfully operationalize ML in healthcare.

This Special Issue addresses two major and interrelated themes. The first is focused upon machine learning research in healthcare, with special emphasis on advances in computer vision, sequence models and transformers, and generalized ML and DL methodologies that apply to non-image and non-temporal data. The second is focused upon challenges of ML operationalization from both clinical and engineering perspectives. In the latter, we welcome submissions related to ML in production, explainable AI, ML monitoring, and integration with clinical workflows.

Our main goal is to stimulate discussion between ML researchers, technologists, and healthcare domain experts, across academia and industry, to meet the common goal of delivering reliable, fair, and high-performance ML applications in healthcare.

Dr. Renato Umeton
Dr. Gregory Antell
Guest Editors

Manuscript Submission Information

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